Declarative external data source importation, exportation, and metadata reflection utilizing HTTP and HDFS protocols

ABSTRACT

Techniques are disclosure for a data enrichment system that enables declarative external data source importation and exportation. A user can specify via a user interface input for identifying different data sources from which to obtain input data. The data enrichment system is configured to import and export various types of sources storing resources such as URL-based resources and HDFS-based resources for high-speed bi-directional metadata and data interchange. Connection metadata (e.g., credentials, access paths, etc.) can be managed by the data enrichment system in a declarative format for managing and visualizing the connection metadata.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation application of U.S. patentapplication Ser. No. 14/864,520, filed on Sep. 24, 2015 and titled“DECLARATIVE EXTERNAL DATA SOURCE IMPORTATION, EXPORTATION, AND METADATAREFLECTION UTILIZING HTTP AND HDFS PROTOCOLS,” which is anon-provisional application of and claims the benefit and priority ofU.S. Provisional Application No. 62/056,476, filed on Sep. 26, 2014 andtitled “DECLARATIVE EXTERNAL DATA SOURCE IMPORTATION, EXPORTATION, ANDMETADATA REFLECTION UTILIZING HTTP AND HDFS PROTOCOLS,” the contents ofwhich are incorporated herein by reference for all purposes.

The present application is related to the following applications:

-   -   1) U.S. Provisional Application No. 62/056,468, filed on Sep.        26, 2014 and titled “METHOD FOR SEMANTIC ENTITY EXTRACTION BASED        ON GRAPH MATCHING WITH AN EXTERNAL KNOWLEDGEBASE AND SIMILARITY        RANKING OF DATASET METADATA FOR SEMANTIC INDEXING, SEARCH, AND        RETRIEVAL”;    -   2) U.S. Provisional Application No. 62/056,471, filed Sep. 26,        2014, entitled “DECLARATIVE LANGUAGE AND VISUALIZATION SYSTEM        FOR RECOMMENDED DATA TRANSFORMATIONS AND REPAIRS”;    -   3) U.S. Provisional Application No. 62/056,474, filed Sep. 26,        2014, entitled “DYNAMIC VISUAL PROFILING AND VISUALIZATION OF        HIGH VOLUME DATASETS AND REAL-TIME SMART SAMPLING AND        STATISTICAL PROFILING OF EXTREMELY LARGE DATASETS”;    -   4) U.S. Provisional Application No. 62/056,475, filed on Sep.        26, 2014 and titled “AUTOMATED ENTITY CORRELATION AND        CLASSIFICATION ACROSS HETEROGENEOUS DATASETS”;    -   5) U.S. Provisional Application No. 62/163,296, filed May 18,        2015 and titled “CATEGORY LABELING”; and    -   6) U.S. Provisional Application No. 62/203,806, filed Aug. 11,        2015 and titled “SIMILARITY METRIC ANALYSIS AND KNOWLEDGE        SCORING SYSTEM.”

The entire contents of the above-identified patent applications areincorporated herein by reference for all purposes.

BACKGROUND

The present disclosure relates generally to data preparation andanalysis. More particularly, techniques are disclosed for declarativeexternal data source importation and exportation.

Before “big data” systems can analyze data to provide useful results,the data needs to be added to the big data system and formatted suchthat it can be analyzed. This data onboarding presents a challenge forcurrent cloud and “big data” systems. Typically, data being added to abig data system is noisy (e.g., the data is formatted incorrectly,erroneous, outdated, includes duplicates, etc.). When the data isanalyzed (e.g., for reporting, predictive modeling, etc.) the poorsignal to noise ratio of the data means the results are not useful. As aresult, current solutions require substantial manual processes to cleanand curate the data and/or the analyzed results. However, these manualprocesses cannot scale. As the amount of data being added and analyzedincreases, the manual processes become impossible to implement.

Processing volumes of data from different collections of data sourcesbecomes a challenge. Different data sources may have different protocolsfor accessibility. Because Big data system may often access data frommany types of sources, the differences in protocols for communicationwith the different data sources introduces challenges for users toimport. The connection information and the credential information mayvary for each data source. Increasingly, the user is burdened to provideconnection information to connect to a data source.

Certain embodiments of the present invention address these and otherchallenges.

BRIEF SUMMARY

The present disclosure relates generally to data preparation andanalysis. More particularly, techniques are disclosed for declarativeexternal data source importation and exportation. The techniquesdisclosed herein enable a user to specify input for identifyingdifferent data sources from which to obtain input data.

The present disclosure discloses a data enrichment service thatextracts, repairs, and enriches datasets, resulting in more preciseentity resolution and correlation for purposes of subsequent indexingand clustering. The data enrichment service can include a visualrecommendation engine and language for performing large-scale datapreparation, repair, and enrichment of heterogeneous datasets. Thisenables the user to select and see how the recommended enrichments(e.g., transformations and repairs) will affect the user's data and makeadjustments as needed. The data enrichment service can receive feedbackfrom users through a user interface and can filter recommendations basedon the user feedback. In some embodiments, the data enrichment servicecan analyze data sets to identify patterns in the data.

The data enrichment service is configured to import and export varioustypes of sources storing resources such as URL-based resources andHDFS-based resources for high-speed bi-directional metadata and datainterchange. Connection metadata (e.g., credentials, access paths, etc.)can be managed by the data enrichment service in a declarative formatfor managing and visualizing the connection metadata. In someembodiments, URL-based resources can be stored on Web Servers andContent Management Systems, Cloud Storage Services exposing REST-basedAPIs, and Apache Hadoop-based HDFS volumes/file systems exposing URL andREST-based APIs for connection.

In some embodiments, a computing system may be implemented for entityclassification and data enrichment of data sets. The computing systemmay implement a data enrichment service. The computing system may beconfigured to implement methods and operations described herein. In someembodiments, a system is disclosed that may include a plurality of datasources and a plurality of data targets. The system may include a cloudcomputing infrastructure system comprising one or more processorscommunicatively coupled to the plurality of data sources andcommunicatively coupled to the plurality of data targets, over at leastone communication network. The cloud computing infrastructure system mayinclude a memory coupled to the one or more processors, the memorystoring instructions to provide a data enrichment service, where theinstructions, when executed by the one or more processors, cause the oneor more processors to perform one or more methods or operationsdescribed herein. Yet other embodiments relate to systems andmachine-readable tangible storage media, which employ or storeinstructions for methods and operations described herein.

In at least one embodiment, a method may include receiving, by a dataenrichment system, a request to access a data source from the dataenrichment system. The request may include identification information ofthe data source and a source type of the data source. The method mayinclude determining, using the identification information, that the dataenrichment system does not have a connection to the data source. Themethod may include rendering a user interface to receive connectioninformation for the data source, the connection information based on thesource type. The method may include receiving, via the user interface, aset of connection parameters, the set of connection parameters includinga connection name, a connection type, a data source location, andcredential information. The source type may include a cloud-basedstorage system, a distributed storage system, a web service, or auniform resource locator (URL). The credential information may includean access key and a password defined for access to the data source. Themethod may include storing the set of connection parameters in a datarepository system accessible to the data enrichment system. Storing theset of connection parameters and the credential information may includegenerating one or more data structures defining a data source, the oneor more data structures including the set of connection parameters andthe credential information. The one or more data structures may bestored in the data repository system. The method may includeestablishing, using the set of connection parameters, a connectionbetween the data enrichment system and the data source. The method mayinclude generating a profile for a data set accessed from the datasource via the connection.

In some embodiments, the method may include generating a data servicecorresponding to the request. The method may include storing, using adata structure in the data repository system, the set of connectionparameters in association with the data service. The method may includegenerating, by the data service, a transformed data set based on thedata set accessed from the data source, where the transformed data setis generated using the profile.

In some embodiments, the method may include querying, using theidentification information, the data repository system to determinewhether the data enrichment system has a connection to the data source.The method may include upon determining that the data enrichment systemhas a connection to the data source, accessing from the data repositorysystem, connection information for the connection. The method mayinclude rendering the user interface to indicate the connectioninformation for the connection to the data source.

In some embodiments, the method may include accessing, using theidentification information, the data repository system to determine thatthe data enrichment system has a plurality of connections to the datasource. The method may include rendering a user interface thatidentifies each of the plurality of connections to the data source. Themethod may include receiving, via the user interface that identifieseach of the plurality of connections, input indicating a selection ofone of the plurality of connections.

The foregoing, together with other features and embodiments will becomemore apparent upon referring to the following specification, claims, andaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a simplified high-level diagram of a data enrichmentsystem, in accordance with an embodiment of the present invention.

FIG. 2 depicts a simplified block diagram of a technology stack, inaccordance with an embodiment of the present invention.

FIG. 3 depicts a simplified block diagram of a data enrichment system,in accordance with an embodiment of the present invention.

FIGS. 4A-4D depict examples of a user interface that providesinteractive data enrichment, in accordance with an embodiment of thepresent invention.

FIGS. 5A-5D depict examples of various user interfaces that providevisualizations of datasets, in accordance with an embodiment of thepresent invention.

FIG. 6 depicts a simplified diagram of a data ingestion system, inaccordance with an embodiment of the present invention.

FIG. 7 depicts an example of a user interface that provides informationabout data ingestion, in according with an embodiment of the presentinvention.

FIG. 8 depicts a data repository model, in accordance with an embodimentof the invention.

FIG. 9 depicts an example of a user interface for managing data servicesand data sources, in accordance with an embodiment of the invention.

FIGS. 10A and 10B depict examples of user interfaces for accessing newdata sources, in accordance with an embodiment of the invention.

FIG. 11 depicts a flowchart of a process of data enrichment, inaccordance with some embodiments of the present invention.

FIG. 12 depicts a simplified diagram of a distributed system forimplementing an embodiment.

FIG. 13 is a simplified block diagram of one or more components of asystem environment in which services may be offered as cloud services,in accordance with an embodiment of the present disclosure.

FIG. 14 illustrates an exemplary computer system that may be used toimplement an embodiment of the present invention.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, specificdetails are set forth in order to provide a thorough understanding ofembodiments of the invention. However, it will be apparent that variousembodiments may be practiced without these specific details. The figuresand description are not intended to be restrictive.

The present disclosure relates generally to a data enrichment servicethat extracts, repairs, and enriches datasets, resulting in more preciseentity resolution and correlation for purposes of subsequent indexingand clustering. In some embodiments, the data enrichment serviceincludes an extensible semantic pipeline, which processes the data in anumber of stages, from ingestion of the data to analysis of the data, topublishing of the data-to-data targets.

In certain embodiments of the present invention, prior to loading datainto a data warehouse (or other data target) the data is processedthrough a pipeline (also referred to herein as a semantic pipeline)which includes various processing stages. In some embodiments, thepipeline can include an ingest stage, prepare stage, profile stage,transform stage, and publish stage. During processing, the data can beanalyzed, prepared, and enriched. The resulting data can then bepublished (e.g., provided to a downstream process) into one or more datatargets (such as local storage systems, cloud-based storage services,web services, data warehouses, etc.) where various data analytics can beperformed on the data. Because of the repairs and enrichments made tothe data, the resulting analyses produce useful results. Additionally,because the data onboarding process is automated, it can be scaled toprocess very large data sets that cannot be manually processed due tovolume.

In some embodiments, data can be analyzed to extract entities from thedata, and based on the extracted entities, the data can be repaired. Forexample, misspellings, address errors, and other common mistakes presenta complex problem to big data systems. For small quantities of data,such errors can be manually identified and corrected. However, in verylarge data sets (e.g., billions of nodes or records) such manualprocessing is not possible. In certain embodiments of the presentinvention, the data enrichment service can analyze data using aknowledge service. Based on the contents of the knowledge service,entities in the data can be identified. For example, an entity can be anaddress, a business name, a location, a person name, an identificationnumber, etc.

FIG. 1 depicts a simplified high-level diagram 100 of a data enrichmentservice, in accordance with an embodiment of the present invention. Asshown in FIG. 1 , a cloud-based data enrichment service 102 can receivedata from various data sources 104. In some embodiments, a client cansubmit a data enrichment request to data enrichment service 102 whichidentifies one or more of the data sources 104 (or portions thereof,e.g., particular tables, datasets, etc.). The data enrichment service102 may then request data to be processed from the identified datasources 104. In some embodiments, the data sources may be sampled, andthe sampled data analyzed for enrichment, making large data sets moremanageable. The identified data can be received and added to adistributed storage system (such as a Hadoop Distributed Storage (HDFS)system) accessible to the data enrichment service. The data may beprocessed semantically by a number of processing stages (describedherein as a pipeline or semantic pipeline). These processing stages caninclude preparation stages 108, enrichment stages 110, and publishingstages 112. In some embodiments, data can be processed in one or morebatches by the data enrichment services. In some embodiments, astreaming pipeline can be provided that processes data as it isreceived.

In some embodiments, a prepare stage 108 can include various processingsub-stages. This may include automatically detecting a data sourceformat and performing content extraction and/or repair. Once the datasource format is identified, the data source can be automaticallynormalized into a format that can be processed by the data enrichmentservice. In some embodiments, once a data source has been prepared, itcan be processed by an enrich stage 110. In some embodiments, inbounddata sources can be loaded into a distributed storage system 105accessible to the data enrichment service (such as an HDFS systemcommunicatively coupled to the data enrichment service). The distributedstorage system 105 provides a temporary storage space for ingested datafiles, which can then also provide storage of intermediate processingfiles, and for temporary storage of results prior to publication. Insome embodiments, enhanced or enriched results can also be stored in thedistributed storage system. In some embodiments, metadata capturedduring enrichment associated with the ingested data source can be storedin the distributed storage system 105. System level metadata (e.g., thatindicates the location of data sources, results, processing history,user sessions, execution history, and configurations, etc.) can bestored in the distributed storage system or in a separate repositoryaccessible to the data enrichment service.

In certain embodiments, the enrichment process 110 can analyze the datausing a semantic bus (also referred to herein as a pipeline or semanticpipeline) and one or more natural language (NL) processors that pluginto the bus. The NL processors can automatically identify data sourcecolumns, determine the type of data in a particular column, name thecolumn if no schema exists on input, and/or provide metadata describingthe columns and/or data source. In some embodiments, the NL processorscan identify and extract entities (e.g., people, places, things, etc.)from column text. NL processors can also identify and/or establishrelationships within data sources and between data sources. As describedfurther below, based on the extracted entities, the data can be repaired(e.g., to correct typographical or formatting errors) and/or enriched(e.g., to include additional related information to the extractedentities).

In some embodiments, a publish stage 112 can provide data sourcemetadata captured during enrichment and any data source enrichments orrepairs to one or more visualization systems for analysis (e.g., displayrecommended data transformations, enrichments, and/or othermodifications to a user). The publishing sub-system can deliver theprocessed data to one or more data targets. A data target may correspondto a place where the processed data can be sent. The place may be, forexample, a location in memory, a computing system, a database, or asystem that provides a service. For example, a data target may includeOracle Storage Cloud Service (OSCS), URLs, third party storage services,web services, and other cloud services such as Oracle BusinessIntelligence (BI), Database as a Service, and Database Schema as aService. In some embodiments, a syndication engine provides customerswith a set of APIs to browse, select, and subscribe to results. Oncesubscribed and when new results are produced, the results data can beprovided as a direct feed either to external web service endpoints or asbulk file downloads.

FIG. 2 depicts a simplified block diagram 200 of a technology stack, inaccordance with an embodiment of the present invention. In someembodiments, the data enrichment service can be implemented using thelogical technology stack shown in FIG. 2 . The technology stack caninclude a user interface/experience (UX) layer 202 that provides accessto the data enrichment service through one or more client devices (e.g.,using a thin client, thick client, web browser, or other applicationexecuting on the client devices). A scheduler service 204 can managerequests/responses received through the UX layer and can manage theunderlying infrastructure on which the data enrichment service executes.

In some embodiments, the processing stages described above with respectto FIG. 1 , can include a number of processing engines. For example, theprepare processing stage 108 can include ingest/prepare engines, aprofiling engine and a recommendation engine. As data is ingested duringprepare processing, the data (or samples thereof) can be stored in adistributed data storage system 210 (such as a “big data” cluster). Theenrich processing stage 110 can include semantic/statistical engines, anentity extraction engine, and repair/transform engines. As describedfurther below, the enrich processing stage 110 can utilize informationobtained from knowledge service 206 during the enrichment process.Enrichment actions (e.g., the addition and/or transformation of data)can be performed on the data stored in the distributed storage system210. Transformation of data may include modification to add missing dataor data to enrich the data. Transformation of data may include modifyingerrors in the data or repairing the data. The publish processing stage112 can include a publish engine, a syndication engine, and a metadataresults manager. In some embodiments, various open source technologiescan be used to implement some functionality within the variousprocessing stages and/or processing engines. For example, file formatdetection can use Apache Tika.

In some embodiments, a management service 208 can monitor changes madeto the data during enrichment processing 110. The monitored changes caninclude tracking which users accessed the data, which datatransformations were performed, and other data. This can enable the dataenrichment service to roll back enrichment actions.

Technology stack 200 can be implemented in an environment such as acluster 210 for big data operations (“Big Data Cluster”). Cluster 210can be implemented using Apache Spark, which provides a set of librariesfor implementing a distributed computing framework compatible with adistributed file system (DFS) such as HDFS. Apache Spark can sendrequests for map, reduce, filter, sort, or Sample cluster processingjobs to effective resource managers like YARN. In some embodiments,cluster 210 can be implemented using a distributed file system offeringsuch as one offered by Cloudera®. The DFS, such as one offered byCloudera®, may include HDFS and Yarn.

FIG. 3 depicts a simplified block diagram of data enrichment system 300,in accordance with an embodiment of the present invention. Dataenrichment system 300 may implement a data enrichment service 302. Dataenrichment service 302 can receive data enrichment requests from one ormore clients 304. Data enrichment service 302 may comprise one or morecomputers and/or servers. Data enrichment service 302 may be a modulethat is comprised of several subsystems and/or modules, including some,which may not be shown. Data enrichment service 302 may have more orfewer subsystems and/or modules than shown in the figure, may combinetwo or more subsystems and/or modules, or may have a differentconfiguration or arrangement of subsystems and/or modules. In someembodiments, data enrichment service 302 may include user interface 306,ingest engine 328, recommendation engine 308, data repository 314,knowledge service 310, profile engine 326, transform engine 322, aprepare engine 312, and publish engine 324. The elements implementingdata enrichment service 302 may operate to implement a semanticprocessing pipeline as described above.

Data enrichment system 300 may include a semantic processing pipeline,in accordance with an embodiment of the present invention. All or partof the semantic processing pipeline may be implemented by dataenrichment service 102. When a data source is added, the data sourceand/or the data stored thereon can be processed through a pipeline priorto loading the data source. The pipeline can include one or moreprocessing engines that are configured to process the data and/or datasource before publishing the processed data to one or more data targets.The processing engines can include an ingest engine that extracts rawdata from the new data source and provides the raw data to a prepareengine. The prepare engine can identify a format associated with the rawdata and can convert the raw data into a format (e.g., normalize the rawdata) that can be processed by the data enrichment service 302. Aprofile engine can extract and/or generate metadata associated with thenormalized data and a transform engine can transform (e.g., repairand/or enrich) the normalized data based on the metadata. The resultingenriched data can be provided to the publish engine to be sent to one ormore data targets. Each processing engine is described further below.

Data enrichment service 302 may include memory that may be volatile(such as random access memory (RAM)) and/or non-volatile (such asread-only memory (ROM), flash memory, etc.). The memory may beimplemented using any type of persistent storage device, such ascomputer-readable storage media. Data enrichment service 302 may alsoinclude or be coupled to additional storage, which may be implementedusing any type of persistent storage device, such as a memory storagedevice or other non-transitory computer-readable storage medium. In someembodiments, local storage may include or implement one or moredatabases (e.g., a document database, a relational database, or othertype of database), one or more file stores, one or more file systems, orcombinations thereof. Computer-readable storage media may includevolatile or non-volatile, removable or non-removable media implementedin any method or technology for storage of information such ascomputer-readable instructions, data structures, program modules, orother data. In some embodiments, the additional storage may beimplemented as a data repository system, such as data repository 314.Data may be stored in the data repository system following a data model,such as one described with reference to FIG. 8 .

In some embodiments, data enrichment service 302 may be provided by acomputing infrastructure system (e.g., a cloud computing infrastructuresystem). The computing infrastructure system may be implemented in acloud computing environment having one or more computing systems. Thecomputing infrastructure system may be communicatively coupled, over oneor more communication networks, to one or more data sources or one ormore data targets such as those described herein.

The clients 304 can include various client devices (such as desktopcomputers, laptop computers, tablet computers, mobile devices, etc.).Each client device can include one or more client applications 304through which the data enrichment service 302 can be accessed. Forexample, a browser application, a thin client (e.g., a mobile app),and/or a thick client can execute on the client device and enable theuser to interact with the data enrichment service 302. The embodimentdepicted in FIG. 3 is merely an example and is not intended to undulylimit the claimed embodiments of the present invention. One of ordinaryskill in the art would recognize many variations, alternatives, andmodifications. For example, there may be more or fewer client devicesthan those shown in the figures.

The client devices 304 may be of various different types, including, butnot limited to personal computers, desktops, mobile or handheld devicessuch as a laptop, a mobile phone, a tablet, etc., and other types ofdevices. A communication network facilitates communications betweenclient devices 304 and data enrichment service 302. The communicationnetwork can be of various types and can include one or morecommunication networks. Examples of communication network 106 include,without restriction, the Internet, a wide area network (WAN), a localarea network (LAN), an Ethernet network, a public or private network, awired network, a wireless network, and the like, and combinationsthereof. Different communication protocols may be used to facilitate thecommunications including both wired and wireless protocols such as IEEE802.XX suite of protocols, TCP/IP, IPX, SAN, AppleTalk, Bluetooth, andother protocols. In general, the communication network may include anycommunication network or infrastructure that facilitates communicationsbetween clients and data enrichment service 302.

A user can interact with the data enrichment service 302 through userinterface 306. Clients 304 can render a graphical user interface todisplay the user's data, recommendations for transforming the user'sdata, and to send and/or receive instructions (“transformationinstructions”) to the data enrichment service 302 through user interface306. The user interfaces disclosed herein, such as those references inFIGS. 4A-4D, 5A-5D, 7, 9, 10A, and 10B, may be rendered by dataenrichment service 302 or via clients 304. For example, a user interfacemay be generated by user interface 306, and rendered by data enrichmentservice 302 at any one of clients 304. A user interface may be providedby data enrichment system 302 via network as part of a service (e.g., acloud service) or a network-accessible application. In at least oneexample, an operator of a data enrichment service 302 may operate one ofclients 304 to access and interact with any user interfaces disclosedherein. The user can send instructions to user interface 306 to add datasources (e.g., provide data source access and/or location information,etc.).

Data enrichment service 302 may ingest data using ingest engine 328.Ingest engine 328 can serve as an initial processing engine when a datasource is added. The ingest engine 328 can facilitate safe, secure, andreliable uploading of user data from one or more data sources 309 intodata enrichment service 302. In some embodiments, ingestion engine 328can extract data from the one or more data sources 309 and store it in adistributed storage system 305 in data enrichment service 302. Dataingested from one or more data sources 309 and/or one or more clients304 can be processed as described above with respect to FIGS. 1-2 andstored in a distributed storage system 305. Data enrichment service 302can receive data from a client data store 307 and/or from one or moredata sources 309. The distributed storage system 305 can serve astemporary storage for the uploaded data during the remaining processingstages of the pipeline, prior to the data being published to one or moredata targets 330. Once an upload is complete, the prepare engine 312 canbe invoked to normalize the uploaded data set.

The received data may include structured data, unstructured data, or acombination thereof. Structure data may be based on data structuresincluding, without limitation, an array, a record, a relational databasetable, a hash table, a linked list, or other types of data structures.As described above, the data sources can include a public cloud storageservice 311, a private cloud storage service 313, various other cloudservices 315, a URL or web-based data source 317, or any otheraccessible data source. A data enrichment request from the client 304can identify a data source and/or particular data (tables, columns,files, or any other structured or unstructured data available throughdata sources 309 or client data store 307). Data enrichment service 302may then access the identified data source to obtain the particular dataspecified in the data enrichment request. Data sources can be identifiedby address (e.g., URL), by storage provider name, or other identifier.In some embodiments, access to a data source may be controlled by anaccess management service. The client 304 may display a request to theuser to input a credential (e.g., username and password) and/or toauthorize the data enrichment service 302 to access the data source.

In some embodiments, data uploaded from the one or more data sources 309can be modified into various different formats. The prepare engine 312can convert the uploaded data into a common, normalized format, forprocessing by data enrichment service 302. Normalizing may be performedby routines and/or techniques implemented using instructions or code,such as Apache Tika distributed by Apache®. The normalized formatprovides a normalized view of data obtained from the data source. Insome embodiments, the prepare engine 312 can read a number of differentfile types. Prepare engine 312 can normalize the data into a characterseparated form (e.g., tab separated values, comma separated values,etc.) or as a JavaScript Object Notation (JSON) document forhierarchical data. In some embodiments, various file formats can berecognized and normalized. For example, standard file formats such asMicrosoft Excel® formats (e.g., XLS or XLSX), Microsoft Word® formats(e.g., DOC or DOCX), and portable document format (PDF), andhierarchical formats like JSON and extended markup language (XML), canbe supported. In some embodiments, various binary encoded file formatsand serialized object data can also be read and decoded. In someembodiments, data can be provided to the pipeline in Unicode format(UTF-8) encoding. Prepare engine 312 can perform context extraction andconversion to the file types expected by data enrichment service 302,and can extract document level metadata from the data source.

Normalizing a data set mat include converting raw data in a data setinto a format that is processable by the data enrichment service 302, inparticular profile engine 326. In one example, normalizing the data setto create a normalized data set includes modifying the data set havingone format to an adjusted format as a normalized data set, the adjustedformat being different from the format. A data set may be normalized byidentifying one or more columns of data in the data set, and modifying aformat of the data corresponding to the columns to the same format. Forexample, data having different formatted dates in a data set may benormalized by changing the formats to a common format for the dates thatcan be processed by profile engine 326. Data may be normalized by beingmodified or converted from a non-tabular format to a tabular format,having one or more columns of data.

Once the data has been normalized, the normalized data can be passed toprofile engine 326. The profile engine 326 can perform a column bycolumn analysis of normalized data to identify the types of data storedin the columns and information about how the data is stored in thecolumns. In this disclosure, although profile engine 326 is described inmany instances as performing operations on data, the data processed byprofile engine 326 has been normalized by prepare engine 312. In someembodiments, the data processed by profile engine 326 may include datathat is not normalized for being in a format (e.g., a normalized format)processable by profile engine 326. The output, or results, of profileengine 326 may be metadata (e.g., source profile) indicating profileinformation about the data from a source. The metadata may indicate oneor more patterns about the data and/or a classification of the data. Asfurther described below, the metadata may include statisticalinformation based on analysis of the data. For example, profile engine326 can output a number of metrics and pattern information about eachidentified column, and can identify schema information in the form ofnames and types of the columns to match the data.

The metadata generated by profile engine 326 may be used by otherelements of data enrichment service, e.g., recommendation engine 308 andtransformation engine 322, to perform operations as described herein fordata enrichment service 302. In some embodiments, the profile engine 326can provide metadata to a recommendation engine 308.

Recommendation engine 308 can identify repair, transform, and dataenrichment recommendations for the data processed by profile engine 326.The metadata generated by profile engine 326 can be used to determinerecommendations for data based on the statistical analysis and/orclassifications indicated by the metadata. In some embodiments,recommendations can be provided to the user through a user interface orother web service. Recommendations can be tailored for business users,such that the recommendations describe at a high level what data repairsor enrichments are available, how those recommendations compare to pastuser activity, and/or how unknown items can be classified based onexisting knowledge or patterns. Knowledge service 310 can access one ormore knowledge graphs or other knowledge sources 340. The knowledgesources can include publicly available information published by websites, web services, curated knowledge stores, and other sources.Recommendation engine 308 can request (e.g., query) knowledge service310 for data that can be recommended to a user for the data obtained fora source.

In some embodiments, transform engine 322 can present the user with thesampled data for each column, or sample rows from the input datasetthrough user interface 306. Through user interface 306, data enrichmentservice 302 may present a user with recommended transformations. Thetransformations may be associated with transformation instructions,which may include code and/or function calls to perform transformationactions. The transformation instructions may be invoked by a user basedon selection at user interface 306, such as by selecting arecommendation for transformation or by receiving input indicating anoperation (e.g., an operator command). In one example, transformationinstructions include a transformation instruction to rename at least onecolumn of data based on the entity information. A further transformationinstruction can be received to rename the at least one column of data toa default name. A default name may include a name that ispre-determined. A default name may be any name that is pre-defined whena name for a column of data cannot be determined or is not defined. Thetransformation instructions can include a transformation instruction toreformat at least one column of data based on the entity information,and a transformation instruction to obfuscate at least one column ofdata based on the entity information. In some embodiments, thetransformation instructions can include an enrichment instruction to addone or more columns of data obtained from the knowledge service based onthe entity information.

Through user interface 306, a user can perform transform actions, andthe transform engine 322 can apply them to the data obtained from a datasource and display the results. This gives the user immediate feedbackthat can be used to visualize and verify the effects of the transformengine 322 configuration. In some embodiments, the transform engine 322can receive pattern and/or metadata information (e.g., column names andtypes) from profile engine 326 and recommendation engine 308, whichprovides recommended transform actions. In some embodiments, transformengine 322 can provide a user event model that orchestrates and trackschanges to the data to facilitate undo, redo, delete, and edit events.The model can capture dependencies between actions so that the currentconfiguration is kept consistent. For example, if a column is removed,then recommended transform actions provided by the recommendation engine308 for that column can also be removed. Similarly, if a transformaction results in inserting new columns and that action is deleted, thenany actions performed on the new columns are also deleted.

As described above, during processing the received data can be analyzedand a recommendation engine 308 can present one or more recommendedtransforms to be made to the data, including enrichment, repair, andother transforms. A recommended transform for enriching data may becomprised of a set of transforms, each transform of which is a singletransform action, or an atomic transformation, performed on the data. Atransform may be performed on data that was previously transformed byanother transform in the set. The set of transforms may be performed inparallel or in a particular order, such that the data resulting afterperforming the set of transforms is enriched. The set of transforms maybe performed according to a transform specification. The transformspecification may include transformation instructions that indicate howand when to perform each of the set of transforms on the data producedby profile engine 326 and the recommendation for enriching the datadetermined by recommendation engine 308. Examples of the atomictransformation may include, without limitation, transforms to headers,conversions, deletions, splits, joins, and repairs. The data that istransformed according to the set of transforms may undergo a series ofchanges, each of which results in intermediate data the data isenriched. The data generated for intermediate steps for the set oftransforms may be stored in a format such as an Resilient DistributedDataset (RDD), text, a data record format, a file format, any otherformat, or a combination thereof.

In some embodiments, the data generated as a result of the operationsperformed by any elements of data enrichment service 302 may be storedin an intermediary data format including, but not limited to, RDD, text,a document format, any other type of format, or a combination thereof.The data stored in the intermediary format may be used to furtherperform operations for data enrichment service 302.

The following tables illustrate examples of transformations. Table 1shows an outline of types of transforms actions.

TABLE 1 Trans- form Function Types Parameter(s) Description ExamplesUpdate String => Update Obfuscate, String column date format, valuesSplit String => Split a Regex split, Array[String] column's delimitervalues into split new columns Filter String => Filter rows White listBoolean based on filtering, a single date range column's filteringvalues Multi- Array[String] => Filter rows NER false column Booleanbased on positives Filter multiple filtering column values EditArray[String] => Edit the Reorder, Columns Array[String] existingremove, columns swap columns Extract (String, String) => Extract valuesNER with Array from a column results [Array[String]] into a newextracted to RDD a new table Insert String => Insert new InsertArray[String] columns timestamp Insert String => Insert new Insert 1:MArray columns in a NER [Array[String]] one-to-many results way

Table 2 shows transform actions that do not fit within the categorytypes shown with reference to Table 1.

TABLE 2 Transform Actions Description Rename column Rename a columnSample Replace the current RDD with a sample of it Join Performs aleft-outer-join between two RDDs Export Export an RDD as columnar datato e.g. HDFS

Table 3 below shows examples of types of transform examples.Specifically Table 3 shows examples of transform actions and describesthe type of transformations corresponding to those actions. For example,a transform action may include filtering data based on detecting thepresence of words from a white list in data. If a user wants to trackcommunications (e.g., tweets) containing “Android” or “iPhone”, atransform action could be added with those two words comprising theprovided white list. This is just one example of the way by which datacould be enriched for a user.

TABLE 3 Transform Actions Description Input Output R1 ObfuscateObfuscate 123-45-6789 ###-##-#### Y sensitive information such as e.g.credit card numbers, ID's, or birth dates Date Reformat a 1330978536Mar. 5, 2012 Y Reformat column 2012 Mar. 12 Mar. 12, 2012 containing a14:13:49 02:13:49 PM date Rename Rename a tagged_0001 user_agent YColumn column text_label_0005 call_letters NER Perform named PopBoothturns Type: Product Y entity your iPhone or Text: PopBooth, recognitioniPad into a iPhone, iPad and insert photo booth, values (see prints andall next section) Search/ Perform search Search: Mozilla Value: YReplace and replace Replace: Godzilla on a column's Godzilla 5.0 valuesValue: Mozilla 5.0 Change Change the case Case: Proper Value: Eden Ycase to lower, upper, Value: eden Prairie or proper prairie White listFilter rows List: Android, Keep all Y filter based on the iPhone rowswhose presence of Value: I values contain words from a heart my“Android” or white list in a iPhone “iPhone” text-valued column

The recommendation engine 308 can use information from a knowledgeservice 310, knowledge source 340 to generate recommendations fortransform engine 322 and to instruct transform engine 322 to generatetransform scripts that will transform the data. Transform scripts mayinclude programs, code, or instructions that may be executable by one ormore processing units to transform received data. As such, therecommendation engine 308 can serve as an intermediary between the userinterface 306 and the knowledge service 310.

As discussed above, profile engine 326 can analyze data from a datasource to determine whether any patterns exist, and if so, whether apattern can be classified. Once data obtained from a data source isnormalized, the data may be parsed to identify one or more attributes orfields in the structure of the data. Patterns may be identified using acollection of regular expressions, each having a label (“tag”) and beingdefined by a category. The data may be compared to different types ofpatterns to identify a pattern. Examples of pattern types that can beidentified include, without limitation, integers, decimals, dates ordate/time strings, URLs, domain addresses, IP addresses, emailaddresses, version numbers, locale identifiers, UUIDs and otherhexidecimal identifiers, social security numbers, US box numbers,typical US street address patterns, zipcodes, US phone numbers, suitenumbers, credit card numbers, proper names, personal information, andcredit card vendors.

In some embodiments, profile engine 326 may identify patterns in databased on a set of regular expressions defined by semantic constraints orsyntax constraints. A regular expression may be used to determine theshape and/or structure of data. Profile engine 326 may implementoperations or routines (e.g., invoke an API for routines that performprocessing for regular expressions) to determine patterns in data basedon one or more regular expressions. For example, a regular expressionfor a pattern may be applied to data based on syntax constraints todetermine whether the pattern is identifiable in the data.

Profile engine 326 may perform parsing operations using one or moreregular expressions to identify patterns in data processed by profileengine 326. Regular expressions may be ordered according to a hierarchy.Patterns may be identified based on order of complexity of the regularexpressions. Multiple patterns may match data that is being analyzed;the patterns having the greater complexity will be selected. Asdescribed further below, profile engine 326 may perform statisticalanalysis to disambiguate between patterns based on the application ofregular expressions that are applied to determine those patterns.

In some embodiments, data that is unstructured may be processed toanalyze metadata-describing attributes in the data. The metadata itselfmay indicate information about the data. The metadata may be compared toidentify similarities and/or to determine a type of the information. Theinformation identified based on the data may be compared to know typesof data (e.g., business information, personal identificationinformation, or address information) to identify the data thatcorresponds to a pattern.

In accordance with an embodiment, the profile engine 326 may performstatistical analysis to disambiguate the patterns and/or the text indata. Profile engine 326 may generate metadata including statisticalinformation based on the statistical analysis. When patterns areidentified, profile engine 326 may determine statistical information(e.g., a pattern metric) about each different pattern to disambiguatebetween the patterns. The statistical information may include a standarddeviation for different patterns that are recognized. The metadataincluding the statistical information can be provided to othercomponents of data enrichment service 302, such as recommendation engine308. For example, the metadata may be provided to recommendation engine308 to enable recommendation engine 308 to determine recommendations forenrichment of the data based on the identified the pattern(s).Recommendation engine 308 can use the patterns to query a knowledgeservice 310 to obtain additional information about the patterns.Knowledge service 310 can include or have access to one or moreknowledge sources 340. A knowledge sources can include publiclyavailable information published by web sites, web services, curatedknowledge stores, and other sources.

Profile engine 326 may perform the statistical analysis to disambiguatebetween patterns identified in the data. For example, data analyzed byprofile engine 326, may be evaluated to compute a pattern metric (e.g.,a statistical frequency of different patterns in the data) for each ofthe different patterns identified in the data. Each of the set ofpattern metrics is computed for a different pattern of the patterns thatare identified. Profile engine 326 may determine a difference amongstthe pattern metrics computed for the different patterns. One of theidentified patterns may be selected based on the difference. Forexample, one pattern may be disambiguated from another pattern based ona frequency of the patterns in the data. In another example, where thedata consists of dates having multiple different formats, eachcorresponding to a different pattern, profile engine 326 may convert thedates to a standard format in addition to normalization and may thendetermine a standard deviation for each format from different patterns.In this example, profile engine 326 may statistically disambiguatebetween the formats in the data as having the format with the loweststandard deviation. The pattern corresponding to the format of the datahaving the lowest standard deviation may be selected as the best patternfor the data.

Profile engine 326 may determine a classification of a pattern that itidentifies. Profile engine 326 may communicate with knowledge service310 to determine whether the identified pattern can be classified withina knowledge domain. Knowledge service 310 may determine one or morepossible domains associated with the data based on techniques describedherein such as matching techniques and similarity analysis. Knowledgeservice 310 may provide profile engine 326 with a classification of oneor more domains possibly similar to data identified with a pattern.Knowledge service 310 may provide, for each of the domains identified byknowledge service 310, a similarity metric indicating a degree ofsimilarity to the domain. The techniques disclosed herein for similaritymetric analysis and scoring can be applied by recommendation engine 308to determine a classification of data processed by profile engine 326.The metadata generated by profile engine 326 may include informationabout the knowledge domain, if any are applicable, and a metricindicating a degree of similarity with the data analyzed by profileengine 326.

Profile engine 326 may perform statistical analysis to disambiguate textidentified in data, regardless of whether patterns are identified in thedata. The text may be part of a pattern, and the analysis of the textmay be used to further identify a pattern, if any can be identified.Profile engine 326 may request knowledge service 310 to perform domainanalysis on text to determine whether the text can be classified intoone or more domains. Knowledge service 310 may operate to provideinformation about one or more domains that are applicable to the textbeing analyzed. Analysis performed by knowledge service 310 to determinea domain may be performed using techniques described herein, such assimilarity analysis used to determine a domain for data.

In some embodiments, profile engine 326 may identify text data in a dataset. The text data may correspond to each entity identified in the setof entities. A classification may be determined for each entity that isidentified. Profile engine 326 may request knowledge service to identifya classification for the entity. Upon determining a set ofclassifications for a set of entities (e.g., entities in a column),profile engine 326 may compute a set of metrics (“classificationmetrics”) to disambiguate between the set of classifications. Each ofthe set of metrics may be computed for a different one of the set ofclassifications. Profile engine 326 may statistically disambiguate theset of metrics by comparing them to each other to determine whichclassification most closely represents the set of entities. Aclassification of the set of entities may be chosen based on theclassification that represents the set of entities.

Using the knowledge sources 340, knowledge service 310 can match, incontext, the patterns identified by profile engine 326. Knowledgeservice 310 may compare the identified patterns in the data or the dataif in text to entity information for different entities stored by aknowledge source. The entity information may be obtained from one ormore knowledge sources 340 using knowledge service 310. Examples ofknown entity may include social security numbers, telephone numbers,address, proper names, or other personal information. The data may becompared to entity information for different entities to determine ifthere is a match with one or more entities based on the identifiedpattern. For example, the knowledge service 310 can match the pattern“XXX-XX-XXXX” to the format of U.S. social security numbers.Furthermore, the knowledge service 310 can determine that socialsecurity numbers are protected or sensitive information, the disclosureof which is linked to various penalties.

In some embodiments, profile engine 326 can perform statistical analysisto disambiguate between multiple classifications identified for dataprocessed by profile engine 326. For example, when text is classifiedwith multiple domains, profile engine 326 can process the data tostatistically determine the appropriate classification determined byknowledge service 310. The statistical analysis of the classificationcan be included in the metadata generated by profile engine 326.

In addition to pattern identification, profile engine 326 can analyzedata statistically. The profile engine 326 can characterize the contentof large quantities of data, and can provide global statistics about thedata and a per-column analysis of the data's content: e.g., its values,patterns, types, syntax, semantics, and its statistical properties. Forexample, numeric data can be analyzed statistically, including, e.g., N,mean, maximum, minimum, standard deviation, skewness, kurtosis, and/or a20-bin histogram if N is greater than 100 and unique values is greaterthan K. Content may be classified for subsequent analysis.

In one example, global statistics may include, without restriction, thenumber of rows, the number of columns, the number of raw and populatedcolumns and how they varies, distinct and duplicate rows, headerinformation, the number of columns classified by type or subtype, andthe number of columns with security or other alerts. Column-specificstatistics may include populated rows (e.g., K-most frequent, K-leastfrequent unique values, unique patterns, and (where applicable) types),frequency distributions, text metrics (e.g., minimum, maximum, meanvalues of: text length, token count, punctuation, pattern-based tokens,and various useful derived text properties), token metrics, data typeand subtype, statistical analysis of numeric columns, L-most/leastprobable simple or compound terms or n-grams found in columns withmostly unstructured data, and reference knowledge categories matched bythis naive lexicon, date/time pattern discovery and formatting,reference data matches, and imputed column heading label.

The resulting profile can be used to classify content for subsequentanalyses, to suggest, directly or indirectly, transformations of thedata, to identify relationships among data sources, and to validatenewly acquired data before applying a set of transformations designedbased on the profile of previously acquired data.

The metadata produced by profile engine 326 can be provided to therecommendation engine 308 to generate one or more transformrecommendations. The entities that match an identified pattern of thedata can be used to enrich the data with those entities identified byclassification determined using knowledge service 310. In someembodiments, the data to the identified patterns (e.g., city and state)may be provided to knowledge service 310 to obtain, from a knowledgesource 340, entities that match the identified patterns. For example,knowledge service 310 may be invoked calling a routine (e.g., getCities() and getStates( )) corresponding to the identified patterns to receiveentity information. The information received from knowledge service 310may include a list (e.g., canonical list) of entities that have properlyspelled information (e.g., properly spelled cities and states) for theentities. Entity information corresponding to matching entities obtainedfrom knowledge service 310 can be used to enrich data, e.g., normalizethe data, repair the data, and/or augment the data.

In some embodiments, the recommendation engine 308 can generatetransform recommendations based on the matched patterns received fromthe knowledge service 310. For example, for the data including socialsecurity numbers, the recommendation engine can recommend a transformthat obfuscates the entries (e.g., truncating, randomizing, or deleting,all or a portion of the entries). Other examples of transformation mayinclude, reformatting data (e.g., reformatting a date in data), renamingdata, enriching data (e.g., inserting values or associating categorieswith data), searching and replacing data (e.g., correcting spelling ofdata), change case of letter (e.g., changing a case from upper to lowercase), and filter based on black list or white list terms. In someembodiments, recommendations can be tailored for particular users, suchthat the recommendations describe at a high level what data repairs orenrichments are available. For example, an obfuscation recommendationmay indicate that the first five digits of the entries will be deleted.In some embodiments, the recommendations can be generated based on pastuser activity (e.g., provide a recommended transform that was previouslyused when sensitive data was identified).

Transform engine 322 can generate transform scripts based on therecommendations provided by recommendation engine 308 (e.g., a script toobfuscate the social security numbers). A transform script may performan operation to transform data. In some embodiments, a transform scriptmay implement a linear transformation of data. A linear transformationmay be implemented through use of an API (e.g., Spark API). Thetransform actions may be performed by operations invoked using the API.A transform script may be configured based on transform operationsdefined using the API. The operations may be performed based on therecommendations.

In some embodiments, the transform engine 322 can automatically generatetransform scripts to repair data at the data source. Repairs may includeautomatically renaming columns, replacing strings or patterns within acolumn, modifying text case, reformatting data, etc. For example, thetransform engine 322 can generate a transformation script to transform acolumn of dates based on a recommendation from recommendation engine 308to modify, or convert, the formats of the dates in the column. Therecommendation may be selected from multiple recommendations to enrichor modify the data from a data source that is processed by profileengine 326. The recommendation engine 308 may determine therecommendation based on metadata, or profile, provided by the profileengine 326. The metadata may indicate a column of dates identified fordifferent formats (e.g., MM/DD/YYYY, DD-MM-YY, etc.). The transformscript generated by transform engine 322 can, for example, split and/orjoin columns based on suggestions from the recommendation engine 308.The transform engine 322 may also remove columns based on the datasource profiles received from profile engine 326 (e.g., to remove emptycolumns, or columns that include information that is not desired by theuser).

A transform script may be defined using a syntax that describesoperations with respect to one or more algorithms (e.g., Spark OperatorTrees). As such, the syntax may describe operator-treetransduction/reduction. A transform script may be generated based on achosen recommendation or requested by a user interactively through agraphical user interface. Examples of recommended transformations aredescribed with reference to FIGS. 4A, 4B, 4C, and 4D. Based on thetransform operations specified by a user through the graphical userinterface, the transform engine 322 performs transform operationsaccording to those operations. The transform operations may berecommended to the user to enrich a data set.

As described further below, the clients 304 can display recommendationsdescribing or otherwise indicating each recommended transform. When auser selects a transform script to be run, the selected transform scriptcan be run on all or more of the data from the data source in additionto the data analyzed to determine the recommended transform(s). Theresulting transformed data can then be published to one or more datatargets 330 by publish engine 324. In some embodiments, the data targetscan be different data stores than the data sources. In some embodiments,the data targets can be the same data stores as the data sources. Datatargets 330 can include a public cloud storage service 332, a privatecloud storage service 334, various other cloud services 336, a URL orweb-based data target 338, or any other accessible data target.

In some embodiments, recommendation engine 308 can query knowledgeservice 310 for additional data related to the identified platform. Forexample, where the data includes a column of city names, related data(e.g., location, state, population, country, etc.) can be identified anda recommendation to enrich the dataset with the related data can bepresented. Examples of presenting recommendations and transforming datathrough a user interface are shown below with respect to FIGS. 4A-4D.

Knowledge service 310 can implement a matching method to compare thedata to reference data available through knowledge service 310.Knowledge service 310 can include or have access to one or moreknowledge sources 340. The knowledge sources can include publiclyavailable information published by web sites, web services, curatedknowledge stores, and other sources. Knowledge service 310 can implementa method to determine the semantic similarity between two or moredatasets. This may also be used to match the user's data to referencedata available through the knowledge service 310. Knowledge service 310may perform similarity metric analysis as described in this disclosure.The techniques performed by knowledge service 310 may include thosedescribed in this disclosure including the techniques described by thereferences incorporated herein.

Knowledge service 310 can perform operations to implement automated dataanalyses. In some embodiments, knowledge service 310 can use anunsupervised machine learning tool, such as Word2Vec, to analyze aninput data set. Word2Vec can receive a text input (e.g., a text corpusfrom a large data source) and generate a vector representation of eachinput word. The resulting model may then be used to identify how closelyrelated are an arbitrary input set of words. For example, a Word2Vecmodel built using a large text corpus (e.g., a news aggregator, or otherdata source) can be utilized to determine corresponding numeric vectorfor each input word. When these vectors are analyzed, it may bedetermined that the vectors are “close” (in the Euclidean sense) withina vector space. Although this can identify that input words are related(e.g., identifying input words that are clustered closely togetherwithin a vector space), Word2Vec may not be usable to identify adescriptive label for the words (e.g., “tire manufacturers”). Knowledgeservice 310 may implement operations to categorize the related wordsusing a curated knowledge source 340 (e.g., YAGO, from the Max PlanckInstitute for Informatics). Using information from a knowledge source340, knowledge service 310 can add additional, related data to the inputdata set.

In some embodiments, knowledge service 310 may implement operations toperform trigram modeling to further refine categorization of relatedterms. Trigram modeling can be used to compare sets of words forcategory identification. The input data set can be augmented with therelated terms.

Using the input data set, which may include added data, knowledgeservice 310 can implement matching methods (e.g., a graph matchingmethod) to compare the words from the augmented data set to categoriesof data from knowledge source 340. Knowledge service 310 can implement amethod to determine the semantic similarity between the augmented dataset and each category in knowledge source 340 to identify a name for thecategory. The name of the category may be chosen based on a highestsimilarity metric. The similarity metric may computed be based on thenumber of terms in the data set that match a category name. The categorymay be chosen based on the highest number of terms matching based on thesimilarity metric. Techniques and operations performed for similarityanalysis and categorization are further described below.

In some embodiments, knowledge service 310 can augment an input data setand use information from a knowledge source 340 to add additional,related data to the input data set. For example, a data analysis toolsuch as Word2Vec can be used to identify semantically similar words tothose included in the input data set from a knowledge source, such as atext corpus from a news aggregation service. In some embodiments,knowledge service 310 can implement trigram modeling to preprocess dataobtained from a knowledge source 340 (such as YAGO) to generate anindexed table of words by category. Knowledge service 310 can thencreate trigrams for each word in the augmented data set and match theword to a word from the indexed knowledge source 340.

Using the augmented data set (or the trigram matched augmented dataset), knowledge service 310 can compare the words from the augmenteddata set to categories of data from knowledge source 340. For example,each category of data in the knowledge source 340 can be represented asa tree structure, with the root node representing the category, and eachleaf node representing a different word belonging to that category.Knowledge service 310 can implement a method (e.g., Jaccard index, orother similarity metric) to determine the semantic similarity betweenthe augmented data set and each category in knowledge source 510. Thename of the category that matches the augmented data set (e.g., having ahighest similarity metric) can then be applied as a label to the inputdata set.

In some embodiments, knowledge service 310 can determine the similarityof two data sets A and B, by determining the ratio of the size of theintersection of the data sets to the size of the union of the data sets.For example, a similarity metric may be computed based on the ratioof 1) the size of the intersection of an data set (e.g., an augmenteddata set) and a category and 2) the size of their union. The similaritymetric may be computed for comparison of a data set and a category asindicated above. As such, a “best match” may be determined based oncomparing the similarity metrics. The data set used for the comparisonmay be enriched by being augmented with a label corresponding to thecategory for which the best match is determined using the similaritymetric

As described above, other similarity metrics may be used in addition, oras an alternative, to the Jaccard index. One of ordinary skill in theart would recognize that any similarity metric may be used with theabove described techniques. Some examples of alternative similaritymetrics include, but are not limited to: the Dice-Sorensen index; theTversky index; the Tanimoto metric; and the cosine similarity metric.

In some embodiments, knowledge service 310 may utilize a data analysistool, such as Word2Vec, to compute a refined metric (e.g., score) thatindicates a degree of match between data from a knowledge source 340 andan input data, which may be augmented with data from a knowledge source.The score (“knowledge score”) may provide greater knowledge about thedegree of similarity between an input data set and a category to which acomparison is made. The knowledge score may enable data enrichmentservice 302 to choose a category name that bests represents the inputdata.

In the techniques described above, knowledge service 310 may count thenumber of matches of terms in the input data set to a candidate category(e.g., genus) name in a knowledge source 340. The result of thecomparison may yield a value that represents a whole integer. As suchthe value, although indicative of the degree of match between terms, maynot indicate a degree of match between an input data set and differentterms in a knowledge source.

Knowledge service 310 may utilize Word2Vec to determine a similarity ofa comparison of each term (e.g., a term for a genus) in a knowledgesource and the terms of input data (e.g., species). Using Word2Vec,knowledge service 310 can compute a similarity metric (e.g., cosinesimilarity or distance) between an input data set and one or more termsobtained from a knowledge source. The cosine similarity may be computedas the cosine angle between a data set of terms (e.g., a domain orgenus) obtained from a knowledge source and an input data set of terms.The cosine similarity metric may be computed in a manner similar to theTanimoto metric. By computing a similarity metric based on a cosinesimilarity, each term in the input data set may be considered as afaction of a whole-value integer, such as a value indicating apercentage of similarity between the term and candidate category. Forexample, computing a similarity metric between a tire manufacturer and asurname might result in a similarity metric of 0.3, while the similaritymetric between a tire manufacturer and a company name might results in asimilarity metric of be 0.5. Non-whole integer values representingsimilarity metrics can be close compared to provide greater accuracy fora closely matching category name. The closely matching category name maybe chosen as the most applicable category name based on the similaritymetric closest to a value of 1. In the example, above, based on thesimilarity metric, company name is more likely the correct category. Assuch, knowledge service 310 can associated “company” instead of“surname” with a user-supplied column of data containing tiremanufactures.

Knowledge service 310 can determine information about knowledge groups(e.g., domains or categories). Information about knowledge groups can bepresented in a graphical user interface. Information about knowledgedomains may include a metric (e.g., a knowledge score) indicating ameasure of similarity between a knowledge domain and an input data setof terms. Input data may be compared to data from a knowledge source340. An input data set may correspond to a column of data of a data setspecified by a user. The knowledge score may indicate a measure ofsimilarity between an input data set and one or more terms provided by aknowledge source, each term corresponding to a knowledge domain. Thecolumn of data may include terms that potentially belong to knowledgedomain.

In at least one embodiment, knowledge service 310 may determine a moreaccurate matching score. The score may correspond to a value computingusing a scoring formula using techniques disclosed herein includingreferences that are incorporated herein. The scoring formula maydetermine a semantic similarity between two data sets, e.g., the inputdata set and terms in a domain (e.g., a candidate category) obtainedfrom a knowledge source. The domain for which the matching scoreindicates the best match (e.g., the highest matching score), may bechosen as the domain having the greatest similarity with the input dataset. As such, the terms in the input data set may be associated with thedomain name as the category.

The scoring formula may be applied to an input data set and a domain(e.g., a category of terms obtained from a knowledge source) todetermine a score that indicates a measure of a match between the inputdata and the domain. The domain may have one or more terms, whichcollectively define the domain. The score may be used to determine thedomain to which an input data set is most similar. The input data setmay be associated with a term descriptive of the domain to which theinput data set is most similar.

In some embodiments, user interface 306 can generate one or moregraphical visualizations based on metadata provided by profile engine326. As explained above, the data provided by profile engine 326 mayinclude statistical information indicating metrics about data that hasbeen processed by profile engine 326. Examples of graphicalvisualizations of metrics of profiled data are shown in FIGS. 5A-5D. Agraphical visualization can include a graphical dashboard (e.g., avisualization dashboard). The graphical dashboard may indicate aplurality of metrics, each of the plurality of metrics indicating a realtime metric of the data relative to a time that the data is profiled. Agraphical visualization may be displayed in a user interface. Forexample, the graphical visualization that is generated may be sent to aclient device to cause the client device to display the graphicalvisualization in a user interface at the client device. In someembodiments, a graphical visualization may provide profiling results.

Additionally, the structural analyses by the profile engine 326 enablethe recommendation engine to better focus its queries to knowledgeservice 310, improving processing speed and reducing load on systemresources. For example, this information can be used to limit the scopeof knowledge being queried so that the knowledge service 310 does notattempt to match a column of numerical data to place names

FIGS. 4A-4D depict examples of a user interface that providesinteractive data enrichment, in accordance with an embodiment of thepresent invention. As shown in FIG. 4A, an example interactive userinterface 400 can display transform scripts 402, recommended transforms404, and at least a portion of the data 406 being analyzed/transformed.Transform scripts listed in panel 402 can include indicate transformsthat have been applied to the data and are visible in panel 406. Eachtransform script 402 can be written in a simple declarative languageintelligible to a business user. Transform scripts listed in panel 402may be automatically applied to the data and reflected in the portion ofthe data 406 displayed in the interactive user interface 400. Forexample, the transform scripts listed in patent 402 include renamingcolumns to be descriptive of their content. Columns 408 shown ininteractive user interface 400 have been renamed according to thetransform scripts 402 (e.g., column 0003 is now named date_time_02,column 0007 is no named “url”, etc.). Recommended transforms 404,however, have not been automatically applied to the user's data.

As shown in FIG. 4B, a user can view recommendations in recommendationpanel 404 and based on the recommendation, identify the data to bechanged. For example, recommendation 410 includes a recommendation torename “Col_0008 to city”. Because the recommendation is written suchthat a business user can understand it (instead of in, e.g., code orpseudo code) the corresponding data 412 can be readily identified by theuser. As shown in FIG. 4B, data 412 includes a column of strings(represented as a row in user interface 400). The profile engine 326 cananalyze the data to determine that it includes strings of two or fewerwords (or tokens). This pattern can be provided to recommendation engine308 which can query knowledge service 310. In this case, knowledgeservice 310 has matched the data pattern to city names andrecommendation 408 was generated to rename the column accordingly.

In some embodiments, transforms listed in panel 404 may have beenapplied at the direction of the user (e.g., in response to aninstruction to apply the transform) or may have been appliedautomatically. For example, in some embodiments, knowledge service 310can provide a confidence score for a given pattern match. A thresholdcan be set in recommendation engine 308 such that matches having aconfidence score greater than the threshold are applied automatically.

To accept the recommendation, the user can select an accept icon 414 (inthis example an up arrow icon) associated with the recommendation. Asshown in FIG. 4C, this moves the accepted recommendation 414 totransform scripts panel 402 and automatically applies the transform tothe corresponding data 416. For example, in the embodiment shown in FIG.4C, Col_0008 has now been renamed to “city” in accordance with theselected transform.

In some embodiments, data enrichment service 302 can recommendadditional columns of data to be added to a data source. As shown inFIG. 4D, continuing with the city example, transforms 418 have beenaccepted to enrich the data with new columns including city population,and city location detail including longitude and latitude. Whenselected, the user's data set is enriched to include this additionalinformation 420. The data set now includes information that was notpreviously available to the user in a comprehensive and automatedfashion. The user's data set can now be used to produce a nationwide mapof locations and population zones associated with other data in thedataset (for example, this may be associated with a company's web sitetransactions).

FIGS. 5A-5D depict examples of various user interfaces that providevisualizations of datasets, in accordance with an embodiment of thepresent invention.

FIG. 5A depicts an example of a user interface that providesvisualizations of datasets, in accordance with an embodiment of thepresent invention. As shown in FIG. 5A, an example interactive userinterface 500 can display a profile summary 502 (“Profile Results”),transform scripts 504, recommended transforms 506, and at least aportion of the data 508 being analyzed/transformed. Transforms listed inpanel 504 can include transforms that have been applied to the data andare visible in panel 508.

Profile summary 502 can include global statistics (e.g., total rows andcolumns) as well as column-specific statistics. The column-specificstatistics can be generated from analysis of data processed by dataenrichment service 302. In some embodiments, the column-specificstatistics can be generated based on column information determined byanalysis of data process by data enrichment service 302.

Profile summary 502 may include a map (e.g., “a heat map”) of the UnitedStates, where different areas of the United States are shown indifferent colors, based on statistics identified from the data beinganalyzed 508. The statistics may indicate how frequently those locationsare identified as being associated with the data. In one illustrativeexample, data may represent purchase transactions at an online retailer,where each transaction can be associated with a location (e.g., based onshipping/billing addresses, or based on recorded IP addresses). Profilesummary 502 may indicate locations of transactions based on processingof the data representing the purchase transactions. In some embodiments,visualizations can be modified based on user input to assist the user insearching the data and finding useful correlations. These features aredescribed further below.

FIGS. 5B, 5C, and 5D show examples of results of interactive dataenrichment for data sets. FIG. 5B shows a user interface 540 that caninclude a profile metric panel 542. Panel 542 can show a summary ofmetrics associated with the selected data source. In some embodiments,as shown in FIG. 5C, a profile metric panel 560 can include metrics fora particular column 562, instead of an entire data set. For example, theuser can select the particular column on the user's client device andthe corresponding column profile 564 can be displayed. In this example,the profiler indicates a 92% match of column_0008 with known cities inthe knowledge source. A high probability in some embodiments can causethe transform engine to automatically label col_0008 to “city”.

FIG. 5D shows a profile metric panel 580 that includes global metrics582 (e.g., metrics related to an entire dataset), and column-specificvisualizations 584. The column specific visualizations 584 can beselected by a user and/or used to navigate the data (e.g., by clicking,dragging, swiping, etc.). The examples described above representsimplified transforms to small data sets. Similar and more complexprocessing can also be applied automatically to large data setscomprising billions of records.

Some embodiments herein, such as those described with reference to FIGS.6-11 , may be described as a process which is depicted as a flowchart, aflow diagram, a data flow diagram, a structure diagram, or a blockdiagram. Although a flowchart may describe the operations as asequential process, many of the operations may be performed in parallelor concurrently. In addition, the order of the operations may bere-arranged. A process is terminated when its operations are completed,but could have additional steps not included in a figure. A process maycorrespond to a method, a function, a procedure, a subroutine, asubprogram, etc. When a process corresponds to a function, itstermination may correspond to a return of the function to the callingfunction or the main function.

The processes depicted herein, such as those described with reference toFIGS. 6-11 , may be implemented in software (e.g., code, instructions,program) executed by one or more processing units (e.g., processorscores), hardware, or combinations thereof. The software may be stored ina memory (e.g., on a memory device, on a non-transitorycomputer-readable storage medium). In some embodiments, the processesdepicted in flowcharts herein can be implemented by a computing systemof a data enrichment service, e.g., data enrichment service 302. Theparticular series of processing steps in this disclosure are notintended to be limiting. Other sequences of steps may also be performedaccording to alternative embodiments. For example, alternativeembodiments of the present invention may perform the steps outlinedabove in a different order. Moreover, the individual steps illustratedin the figures may include multiple sub-steps that may be performed invarious sequences as appropriate to the individual step. Furthermore,additional steps may be added or removed depending on the particularapplications. One of ordinary skill in the art would recognize manyvariations, modifications, and alternatives.

In an aspect of some embodiments, each process in FIGS. 6-11 can beperformed by one or more processing units. A processing unit may includeone or more processors, including single core or multicore processors,one or more cores of processors, or combinations thereof. In someembodiments, a processing unit can include one or more special purposeco-processors such as graphics processors, digital signal processors(DSPs), or the like. In some embodiments, some or all of processingunits can be implemented using customized circuits, such as applicationspecific integrated circuits (ASICs), or field programmable gate arrays(FPGAs).

FIG. 6 depicts a simplified diagram of a data ingestion system 600, inaccordance with an embodiment of the present invention. Data ingestionsystem 600 can be implemented using one or more of elements of dataenrichment service 302. As shown in FIG. 6 , ingest engine 328 of dataenrichment service 302 can identify one or more data sources to beingested through an interface 606 (e.g., interface 306). Interface 606may have an application programming interface (API), such as thosedescribed below with reference to FIGS. 9-10 .

A request for ingestion of data from a data source can be receivedthrough various mechanisms, such as through a web interface 602 orthrough a scheduler service 604. Ingest engine 328 can include one ormore data source plugins 608 enabling ingest engine 328 to obtain one ormore data sets from a data source. Each of the plugins 608 can becustomized to access various different types of data sources. Ingestengine 328 can be extensible and additional plugins can be added tosupport new types of data sources.

In some embodiments, ingest engine 328 can be configured to manageinformation about the ingestion process and in doing so, is able todetermine metrics about the ingestion process. Ingest engine 328 mayprovide metrics about the ingestion process, such as a number of filesingested, size of the ingested content, and time taken for uploading.For example, ingestion engine 328 may determine file statistics, such asthose shown in the interface described with reference to FIG. 7 .

In some embodiments, ingest engine 328 can receive via a user interface(e.g., web interface 602) a request to access a data source. The requestmay be received through a user interface such as those described withreference to FIGS. 9-10 . Through the user interface, the request mayinclude identification information about the data source and a type ofdata source (“source type”). Ingest engine 328 may query data repository314 using the identification information to determine whether dataenrichment service 302 has any connections to a requested data source.Ingest engine 328 may operate with the assistance of one of data sourceplugins 608 determined based on the source type. A data source plugin608 may be executed to handle requests to access a data source havingthe source type. In some embodiments, data enrichment service 302 mayhave a plurality of connections to a data source.

Upon determining that the data enrichment service does not have aconnection to the data source, ingest engine 328 may render a userinterface at web interface 602 to receive connection information for thedata source based on the source type. Via the user interface, a set ofconnection parameters is received. The set of connection parametersinclude a connection name, a connection type, a data source location,and credential information. The credential information may include anaccess key and a password defined for access to the data source. Upondetermining that one or more connections exist to the data source,ingest engine 328 may obtain the connection information from datarepository 312 to identify the connection(s). The connection informationmay be rendered in a user interface at web interface 602. In someembodiments, the user interface may enable a user to select a connectionto the data source.

Once data is ingested, ingestion engine 328 can store the ingestedcontent and/or metadata in a distributed storage system (e.g.,distributed storage system 305) temporarily, while the content and/ormetadata is processed prior to being published.

FIG. 7 depicts an example of a user interface 700 that providesinformation about data ingestion, in according with an embodiment of thepresent invention. User interface 700 may be a graphical user interfacethat displays one or more graphical visualizations about data ingestion.User interface 700 may be rendered by data enrichment service 302. Userinterface 700 can display metrics related to the data sets that arebeing ingested. For example, user interface 700 may be displayed havingone or more graphical visualizations about one or more metrics relatedto the types of files (e.g., .xlsx, .tsv, .xls, .pdf, .doc, .csv, or.txt) that are being ingested. For example, user interface 700 mayinclude a graphical visualization 702 that indicates a population offile types for the data ingested from one or more data sources. Userinterface 700 may include a graphical visualization 704 that indicates adistribution of the size of files ingested by data enrichment service302. User interface 700 may include a graphical visualization 706 thatindicates a number of rows processed per file. In some embodiments, userinterface 700 may include one or more interactive elements to adjust thegraphical visualizations and/or to specify criteria (e.g., a type ofgraphical visualization or a time period) desired for viewinginformation about data processed by data enrichment service 302.

FIG. 8 depicts a data repository model 800, in accordance with anembodiment of the invention. The example data repository model 800defines and indicates a relationship between data structures formanaging data sources, data targets, data services, and jobs involving aparticular service and source, run on demand or according to aparticular schedule.

Data repository model 800 may be implemented by data repository 314 indata enrichment service 302. In some embodiments, all or some of theelements of data enrichment service 302 may operate to implement datarepository model 800. Data repository model 800 may be implemented bycreating one or more data structures stored in association with therelationships depicted in FIG. 8 . Model 800 is shown as an example andmay include more or fewer data structures in a different arrangementthan shown. The tables shown below for the data structures in model 800illustrate the type of data that in stored in the data structures.

As described above, a data source can be any source of input data. Datasources can be defined as a data site 808 (DATA_SITE). A data site 808can be instantiated defining a protocol specifier (e.g. HTTP), location,path, and/or pattern that specifies a collection of data in associatedwith the data site. An example of a data structure for data site 808 isshown below in Table 4. Data site 808 holds information about aconnection to a data source.

TABLE 4 DATA_SITE P* SITE_NAME VARCHAR2 (400 CHAR) DESCRIPTION VARCHAR2(2000 CHAR) * SITE_TYPE VARCHAR2 (50) * CREATED TIMESTAMP * CREATED_BYVARCHAR2 (500 CHAR) MODIFIED TIMESTAMP MODIFIED_BY VARCHAR2 (500 CHAR)

 DATA_SITE_PK (SITE_NAME)

One example of a data site 808 is a database site 818 (DATABASE SITE),which specifies input from an RDBMS with connection information and anSQL query. Database site 818 can hold specific connection informationfor a database connection. An example of database site 818 is shownbelow in Table 5.

TABLE 5 DATABASE_SITE UF* SITE_NAME VARCHAR2 (400 CHAR) * DB_VENDORVARCHAR2 (1000 CHAR) * DRIVER_NAME VARCHAR2 (1000 CHAR) * CONN_STRVARCHAR2 (1000 CHAR) * USER_NAME VARCHAR2 (500 CHAR) * PASSWD VARCHAR2(1000 CHAR)

 DATABASE_SITE__UN (SITE_NAME)

 DATABASE_SITE__DATA_SITE_FK (SITE_NAME)

 DATABASE_SITE__IDX (SITE_NAME)

Other examples of a data site 808 include: an HTTP site (HTTP_SITE),which specifies input via HTTP, including host, port, credentials, and afile path plus a file name GLOB pattern; and a FILE_SITE, which includeseither a local, NFS mounted, or HDFS file system, as specified by a URIand a collection of data specified by path and a file name GLOB pattern.In some embodiments, access credentials may also be maintained forvarious file systems under the FILE_SITE. Although three data siteexamples are provided, these are for illustrative purposes only and arenot limiting, additional or alternative data sites may also beimplemented.

An example of FILE_SITE is shown below in table 6.

TABLE 6 Formula DT Domain (Default No Column name PK FK M Data Type kindName Value) 1 SITE_NAME F Y VARCHAR (400 CHAR) LT 2 URI Y VARCHAR (2000CHAR) LT 3 FILEPATH VARCHAR (2000 CHAR) LT 4 FILENAMEPATTERN VARCHAR(2000 CHAR) LT Indexes Index Name State Functional Spatial ExpressionFILE_SITE_UN UK SITE_NAME FILE_SITE_IDX SITE_NAME Foreign Keys(referring to) Name Referring To Mandatory Transferable In ArcFILE_SITE_DATA_SITE_FK DATA_SITE Y Y Y

Database SQL 824 (DATABASE_SQL) can store SQL statements used to selectand insert data on a database connection. An example of database SQL 824is shown below in Table 7.

TABLE 7 DATABASE_SQL P* SQL_NAME VARCHAR2 (400 CHAR) PF* SITE_NAMEVARCHAR2 (400 CHAR) DESCRIPTION VARCHAR2 (2000 CHAR) * SQLSTMNT VARCHAR2(4000 CHAR)

 DATABASE_SQL_PK(SQL_NAME. SITE_NAME)

 DATABASE_SQL_DATABASE_SITE_FK (SITE_NAME)

 DATABASESQL_IDX (SITE_NAME)

A data source 806 (DATA_SOURCE) may reference data site 808 where thedata source is located. In some embodiments, a data source 806 may bedefined by a query or pattern, and as such may include data retrievedfrom multiple physical or virtual data repositories. An example of datasource 806 is shown below in Table 8.

TABLE 8 DATA_SOURCE P* SOURCE_NAME VARCHAR2 (400 CHAR) F* SITE_NAMEVARCHAR2 (400 CHAR) * CREATED TIMESTAMP * CREATED_BY VARCHAR2 (500 CHAR)MODIFIED TIMESTAMP MODIFIED_BY VARCHAR2 (500 CHAR) JSON CLOB

 DATA_SOURCE_PK(SOURCE_NAME)

 DATA_SOURCE_DATA_SITE_FK (SITE_NAME)

 DATA_SOURCE__IDX (SITE_NAME)

In some embodiments, data source 806 may be further defined by sourcefile 804 (SOURCE_FILE) that indicates a location of the data source.Source file 804 may reference data source 806. An example of source file804 is shown below in Table 9.

TABLE 9 SOURCE_FILE PF* FILE_ID NUMBER F* SOURCE_NAME VARCHAR2 (400CHAR) PROCESSED_ON TIMESTAMP

 SOURCE_FILE_PK (FILE_ID)

 FK_ASS_4 (FILE_ID)

 SOURCE_FILE_DATA_SOURCE_FK (SOURCE_N)

URL site 810 (URL_SITE) may reference data site 806 where the URL sitemay be accessed. An example of URL site 810 is shown below withreference to Table 10.

TABLE 10 URI_SITE UF* SITE_NAME VARCHAR2 (400 CHAR) * URI VARCHAR2 (1000CHAR) * ANONYMOUS CHAR (1) USER_NAME VARCHAR2 (500 CHAR) PASSWD VARCHAR2(1000 CHAR) FILEPATH VARCHAR2 (2000 CHAR) FILENAMEPATTERN VARCHAR2 (2000CHAR)

 URI_SITE_UN (SITE_NAME)

 URI_SITE_DATA_SITE_FK (SITE_NAME)

 URI_SITE_IDX (SITE_NAME)

In some embodiments, model 800 may store a data structure for a datatarget 802 (DATA_TARGET). In some embodiments, a data target 802 can bedefined to be a specific data store, table, file, or collection offiles. Data target 802 may reference data site 808, which also stores adata source. An example of data target 802 is shown below in Table 11.

TABLE 11 DATA_TARGET P* TARGET_NAME VARCHAR2 (400 CHAR) F* SITE_NAMEVARCHAR2 (400 CHAR) * CREATED TIMESTAMP * CREATED_BY VARCHAR2 (500 CHAR)MODIFIED TIMESTAMP MODIFIED_BY VARCHAR2 (500 CHAR)

 DATA_TARGET_PK(TARGET_NAME)

 DATA_TARGET_DATA_SITE_FK (SITE_NAME)

 DATA_TARGET_IDX (SITE_NAME)

Data target 802 may be further defined by target file 812 (TARGET_FILE).Target file 812 may identify a target file. Target file 812 mayreference data target 802. An example of target file 812 is shown belowin table 12.

TABLE 12 TARGET_FILE PF* FILE_ID NUMBER F* JOB_ID NUMBER F* TARGET_NAMEVARCHAR2 (400 CHAR) PUBLISHED_ON TIMESTAMP

 TARGET_FILE_PK (FILE_ID)

 FK_ASS_5 (FILE_ID)

 TARGET_FILE_DATA_TARGET_FK (TARGET_NAM

 TRG_FILE_DATA_SRV_JOB_FK (JOB_ID)

In some embodiments, target file 812 and/or source file 804 mayreference a data file 814 (DATA_FILE). Data file 814 may serve as a filefor data target 802 and data source 806. An example of data file 814 isshown below in table 13.

TABLE 13 DATA_FILE P* FILE_ID NUMBER * URI VARCHAR2 (2000 CHAR) *DELETED CHAR (1) F* PROFILE_ID NUMBER * FILE_TYPE VARCHAR2 (6 CHAR)

 DATA_FILE_PK (FILE_ID)

 DATA_FILE_PROFILE_FK (PROFILE_ID)

Data file 814 may be defined based on a profile 822 (PROFILE). Profile822 may be a specific type of data file. Profile 822 may further definea data file. An example of profile 822 is shown below in table 14.

TABLE 14 PROFILE P* PROFILE_ID NUMBER * URI VARCHAR2 (2000 CHAR) *CREATED TIMESTAMP * CREATED_BY VARCHAR2 (500 CHAR) JSON CLOB

 PROFILE_PK (PROFILE_ID)

Data service schedule 816 (DATA_SERVICE_SCHEDULE) can store dataindicating an association between a data source 806 and a data target802 with a data service 828. TData service schedule 816 defines aperiodic schedule of runtime jobs. The schedule is modeled on a CRON jobschedule with integers for minute, hour, day of month, month, and day ofweek, linking jobs with data services by way of profile and transformids. Data enrichment service 302 may generate a data service 828corresponding to a request by a user. An example of a data serviceschedule is shown below in table 15.

TABLE 15 DATA_SERVICE_SCHEDULE PF* DATA_SERVICE_NAME VARCHAR2 (400 CHAR)PF* DATA_SERVICE_VERSION INTEGER PF* TARGET_NAME VARCHAR2 (400 CHAR) PF*SOURCE_NAME VARCHAR2 (400 CHAR) MI INTEGER HR INTEGER MM INTEGER DMINTEGER DW INTEGER * CREATED TIMESTAMP * CREATED_BY VARCHAR2 (500 CHAR)MODIFIED TIMESTAMP MODIFIED_BY VARCHAR2 (500 CHAR)

 DATA_SERVICE_SCHEDULE_PK (TARGET_NAME. DATA_SERVIC

 DATA_SRV_SCHED_DATA_SRC_FK (SOURCE_NAME)

 DATA_SRV_SCHED_DATA_TARGET_FK (TARGET_NAME)

 DATA_SRV_SCHED_DATA_SRV_FK(DAT_SERVICE_NAME. DAT

 DATA_SERVICE_SCHEDULE_IDX (SOURCE_NAME)

 DATA_SERVICE_SCHEDULE_IDXV1 (TARGET_NAME)

 DATA_SERVICE_SCHEDULE_IDXV2 (DATA_SERVICE_NAME)

Connection information, such as connection parameters for connecting toa data source may be stored in association with a data service 828 viadata service schedule 816. An example of data service 828 is shown belowin table 16.

TABLE 16 DATA_SERVICE P* DATA_SERVICE_NAME VARCHAR2 (400 CHAR P*DATA_SERVICE_VERSION INTEGER DESCRIPTION VARCHAR2 (2000 CHA F*PROFILE_ID NUMBER F* TRANSFORM_NAME VARCHAR2 (500 CHAR * CREATEDTIMESTAMP * CREATED_BY VARCHAR2 (500 CHAR MODIFIED TIMESTAMP MODIFIED_BYVARCHAR2 (500 CHAR JSON CLOB

 DATA_SERVICE_PK (DATA_SERVICE_NAME. DATA_SER

 DATA_SERVICE_PROFILE_FK (PROFILE_ID)

 DATA_SERVICE_TRANSFORM_FK (TRANSFORM_NAME)

 DATA_SERVICE_IDX (PROFILE_ID)

 DATA_SERVICE_IDXv1 (TRANSFORM_NAME)

Data service 828 may generate a transformed data set (e.g., transform826) based on a data set accessed from a data source 806. Thetransformed data set may be generated using the profile 822. In someembodiments, data published to a data target represents the transformedoutput of the data from the data source. In some embodiments, dataservice 828 can include a profile 814, transform 826, and otherinformation provided by the user and/or derived or inferred by the dataenrichment service 302. At design time, a service can be created byspecifying a transform (e.g., a collection of one or more atomictransformation actions) that is associated with data from a data sourceand described by a profile. In some embodiments, the transform can be aURI pointer to an underlying object or file that defines the transform(e.g., a JxfeTransformEngineSpec object or other object or file). Insome embodiments, a profile may also include a URI pointer to anunderlying object or file that defines the profile. Although any servicemay be executed on any source, in some embodiments a runtime profile canmatch the design time profile. An example of transform 826 is shownbelow in table 17.

TABLE 17 TRANSFORM P* TRANSFORM_NAME VARCHAR2 (500 CHAR) * URI VARCHAR2(2000 CHAR) * CREATED TIMESTAMP * CREATED_BY VARCHAR2 (500 CHAR) JSONCLOB

 TRANSFORM_PK (TRANSFORM_NAME)

In some embodiments, a data service schedule 816 can define a periodicschedule of runtime jobs. The schedule can be modeled on a job schedulerwith, e.g., integers for minute, hour, day of month, month, and day ofweek. The data service schedule object can link operations defined bydata services using profile and transform identifiers (such as the URIsdescribed above). In some embodiments, execution information on each jobmay be stored in a data service job object, including a set of metricsrelated to the job, the job's status, and any error or warning messages.A data service job 830 can include the execution of one or moresub-jobs, including profile, transform, and prepare operations. Anexample of data service job 830 is shown below in table 18.

TABLE 18 DATA_SERVICE_JOB P* JOB_ID NUMBER F* TARGET_NAME VARCHAR2 (400CHAR) F* DATA_SERVICE_NAME VARCHAR2 (400 CHAR) F* DATA_SERVICE_VERSIONINTEGER F* SOURCE_NAME VARCHAR2 (400 CHAR) * STARTED TIMESTAMP ENDEDTIMESTAMP FILESIZE INTEGER NUMROWS INTEGER NUMCOLUMNS INTEGER STATUSVARCHAR2 (255 CHAR) MESSAGE VARCHAR2 (4000 CHAR)

 DATA_SERVICE_JOB_PK (JOB_ID)

 DATA_SRV_JOB_DATA_SRV_SCHED_FK (TARGET_NAME.

 DATA_SERVICE_JOB_IDX (TARGET_NAME.DATA_SERVIC

A data service job 830 may be defined as a job application 832(JOB_APPLICATION). Job application 832 may reference data service job830. Execution information on each job may be found in data service job830, including a minimal set of metrics on the job, its status, and anyerror or warning messages. Each job includes start and end times. Anexample of job application 832 is shown below in table 19.

TABLE 19 JOB_APPLICATION PF* JOB_ID NUMBER P* YARN_APP_ID VARCHAR2 (255CHAR) APP_NAME VARCHAR2 (500 CHAR) STARTED TIMESTAMP FINISHED TIMESTAMP

 JOB_APPLICATION_PK (JOB_ID. YARN_APP_ID)

 JOB_APP_DATA_SRV_JOB_FK (JOB_ID)

 JOB_APPLICATION_IDX (JOB_ID)

Parameter 820 stores information about system parameters. Parameter 820may store information about job 830, profile 822, service, or othersituation-specific parameters for running various engines. An example ofparameter 820 is shown below in table 20.

TABLE 20 PARAMETER P* PARAM_NAME VARCHAR2 (400 CHAR) DESCRIPTIONVARCHAR2 (1000 CHAR) * VALUE_TYPE VARCHAR2 (50 CHAR) * PARAM_VALUEVARCHAR2 (1000 CHAR) * USER_UPDATABLE CHAR (1) DEFAULT_VALUE VARCHAR2(1000 CHAR)

 PARAMETER_PK (PARAM_NAME)

FIG. 9 depicts an example user interface 900 for managing data servicesand data sources, in accordance with an embodiment of the invention. Asshown in FIG. 9 , a user interface 900 for managing data services anddata sources can be displayed to a user at any one of clients 304through an application, such as a web browser, a mobile app, or anyother application.

User interface 900 may enable data curators to manage data curation frommultiple data sources. User interface 900 may include one or more panes,such as a services pane 902, a profiles pane 904, and a data source pane906. User interface 900 provides a user with access to a centralizedresource through which a user can manage their data sources, dataservices that are operating using those data sources, and profileinformation for the data and metadata stored in the data sources.

User interface 900 may be used to add a new data source to dataenrichment service 302. The new data source may become accessible todata enrichment service 302 upon being added to data enrichment service302. Services pane 902 displays the data services for which a user withaccess to data enrichment service 302 has read or/and write access.Services pane 902 may include one or more interactive elements to selecta data services accessible to the user.

Data source pane 906 may display one or more data sources for which theuser has read or/and write access. The data sources may include thoseaccessible to the user based on the access entitled to the user. Datasource pane 906 may include interactive elements to provide informationto manage (e.g., add, remove, or modify) data sources for dataenrichment service 302. Interaction with data source pane 906 may leadto other user interfaces to be displayed, such as those described withreference to FIGS. 10A and 10B.

Profiles pane 904 may provide information indicating profile informationdetermined for data ingested by data enrichment service 302. Profilespane 904 may provide graphical visualizations that indicate a profile ofdata that has been profiled by data enrichment service 302. In someembodiments, profiles pane 904 may include one or more interactiveelements to control display of profile information. The interactiveelement(s) may cause one or more user interfaces to be displayed, suchas those described with reference to FIGS. 5A-5D.

FIGS. 10A and 10B depict examples of user interfaces to access new datasources, in accordance with an embodiment of the invention.Specifically, FIGS. 10A and 10B, show an example of a series of userinterfaces that enable a user to add a new data source, in accordancewith an embodiment of the invention. Beginning with FIG. 10A, a userinterface 1002 may be rendered based on interaction with user interface900. User interface 1002 may include one or more interactive elementsthat enable a user to indicate an existing data source and/or specify anew data source. A user can specify a name for the data source andindicate whether it is a new data source or an existing data source(e.g., to be modified or updated).

Upon selecting an existing data source, a user may be presented withanother user interface that enables a user to choose from data sourcesaccessible to data enrichment service 302. Upon choosing a new datasource, a user interface 1004 may be rendered. User interface 1004 mayinclude one or more interactive elements to enable a user to provide asource type, such as a public or private cloud-based storage service,HDFS, web service, URL, etc. In some embodiments, after selecting asource type, user interface 1004 may include one or more interactiveelements to receive credential data specific to that source type (e.g.,one or more of connection information, query information, host, port, afile path, URL, URI, etc.). The credential data may include one or morepassword, keys, tokens, codes, or the like.

Upon receiving a selection of a source type, user interface 1006 may berendered. As shown in FIG. 10B, user interface 1006 may include one ormore interactive elements to receive input to establish a connection tothe data source(s) (such as an existing connection to a cloud storageservice) chosen using the user interfaces in FIG. 10A. User interface1006 may display one or more interactive elements 1010, 1012 to selectan existing connection, which is associated with connection metadataabout an established connection to a chosen data source. User interface1004 may include one or more interactive elements 1014 to establish anew connection.

Upon interaction with user interface 1006, a user interface, such asuser interface 1008 may be rendered, to configure a connection dependingon the option selected in user interface 1006. In the example shown inFIG. 10B, user interface 1008 includes one or more interactive elementsto receive input for configuring a new connection to a selected datasource. In some embodiments, a user interface may be displayed withinteractive elements to configure existing and/or new parameters for anexisting connection. User interface 1008 may include interactiveelements including an interactive element 1020 to configure a connectionname, an interactive element 1022 to configure a connection type, aninteractive element 1024 to configure an endpoint identifying a datasource, and interactive elements 1026, 1028 to configure credentialinformation (e.g., an access key and/or a secret key). Credentialinformation may include one or more password, keys, tokens, codes, orthe like. User interface 1008 may include an interactive element to testa connection to the data source using the parameters specified for thenew connection.

FIG. 11 illustrates a flowchart 1100 of a process for data enrichment,according to an embodiment of the present invention. The processdepicted in flowchart 1100 may begin at 1102 by receiving a request toaccess a data source from a data enrichment system. The request caninclude identification information of the data source and a source typeof the data source. The identification information may include a name ofa data source or any other information identifying a data source. Theidentification may be received as input via a user interface, e.g., auser interface depicted in FIG. 10A. The source type may include acloud-based storage system, a distributed storage system, a web service,or a uniform resource locator (URL).

At step 1104, using the identification information, a determination ismade that the data enrichment system does not have a connection to thedata source. Using the identification information, a data repositorysystem accessible to the data enrichment system may be queried todetermine whether the data enrichment system has a connection to thedata source. The data repository system may be accessed to determinethat the data enrichment system one or more connections to the datasource. Upon determining that the data enrichment system does not have aconnection to the data source, at step 1106, a user interface isrendered to receive connection information for the data source, theconnection information based on the source type. At step 1108, via theuser interface, a set of connection parameters is received. The set ofconnection parameters include a connection name, a connection type, adata source location, and credential information. The credentialinformation may include an access key and a password defined for accessto the data source.

At step 1110, the set of connection parameters are stored in the datarepository system accessible to the data enrichment system. Connectionparameters may be stored in the data repository system according to adata repository model, such as the one described with reference to FIG.8 . Storing the set of connection parameters may include generating oneor more data structures defining a data source. The data structures mayinclude those depicted in FIG. 8 . The data structures may store the setof connection parameters and the credential information.

At step 1112, using the set of connection parameters, a connectionbetween the data enrichment system and the data source may beestablished. Based on the source type, the data enrichment system cancommunicate with the data source identified by the connectionparameters.

In some embodiments, upon determining that the data enrichment systemhas a connection to the data source, connection information for theconnection may be accessed from the data repository system using theidentity information. In some embodiments, the data repository systemmay be accessed to determine that the data enrichment system has aplurality of connections to the data source. A user interface may berendered that indicates the connection information for each of the oneor more connections existing to the data source. In the instance wherethere are multiple connections to a data source, input may be receivedvia the user interface. The input may indicate a selection of one of theconnections to the data source.

One or more data sets may be accessed from the data source using theconnections to the data source that are either established or alreadyexisting. At step 1114, a profile for a data set access from the datasource via a connection may be generated.

In some embodiments, a data service may be generated corresponding tothe request to access the data source. Using a data structure in thedata repository system, the set of connection parameters may be storedin association with the data service. The data service may generate atransformed data set based on the data set accessed from the datasource. The transformed data set may be generated using the profile.

The process may end at 1116.

FIG. 12 depicts a simplified diagram of a distributed system 1200 forimplementing an embodiment. In the illustrated embodiment, distributedsystem 1200 includes one or more client computing devices 1202, 1204,1206, and 1208, which are configured to execute and operate a clientapplication such as a web browser, proprietary client (e.g., OracleForms), or the like over one or more network(s) 1210. Server 1212 may becommunicatively coupled with remote client computing devices 1202, 1204,1206, and 1208 via network 1210.

In various embodiments, server 1212 may be adapted to run one or moreservices or software applications such as services and applications thatprovide the document (e.g., webpage) analysis and modification-relatedprocessing. In certain embodiments, server 1212 may also provide otherservices or software applications can include non-virtual and virtualenvironments. In some embodiments, these services may be offered asweb-based or cloud services or under a Software as a Service (SaaS)model to the users of client computing devices 1202, 1204, 1206, and/or1208. Users operating client computing devices 1202, 1204, 1206, and/or1208 may in turn utilize one or more client applications to interactwith server 1212 to utilize the services provided by these components.

In the configuration depicted in FIG. 12 , software components 1218,1220 and 1222 of system 1200 are shown as being implemented on server1212. In other embodiments, one or more of the components of system 1200and/or the services provided by these components may also be implementedby one or more of the client computing devices 1202, 1204, 1206, and/or1208. Users operating the client computing devices may then utilize oneor more client applications to use the services provided by thesecomponents. These components may be implemented in hardware, firmware,software, or combinations thereof. It should be appreciated that variousdifferent system configurations are possible, which may be differentfrom distributed system 1200. The embodiment shown in FIG. 12 is thusone example of a distributed system for implementing an embodimentsystem and is not intended to be limiting.

Client computing devices 1202, 1204, 1206, and/or 1208 may includevarious types of computing systems. For example, client device mayinclude portable handheld devices (e.g., an iPhone®, cellular telephone,an iPad®, computing tablet, a personal digital assistant (PDA)) orwearable devices (e.g., a Google Glass® head mounted display), runningsoftware such as Microsoft Windows Mobile®, and/or a variety of mobileoperating systems such as iOS, Windows Phone, Android, BlackBerry 10,Palm OS, and the like. The devices may support various applications suchas various Internet-related apps, e-mail, short message service (SMS)applications, and may use various other communication protocols. Theclient computing devices may also include general purpose personalcomputers including, by way of example, personal computers and/or laptopcomputers running various versions of Microsoft Windows®, AppleMacintosh®, and/or Linux operating systems. The client computing devicescan be workstation computers running any of a variety ofcommercially-available UNIX® or UNIX-like operating systems, includingwithout limitation the variety of GNU/Linux operating systems, such asfor example, Google Chrome OS. Client computing devices may also includeelectronic devices such as a thin-client computer, an Internet-enabledgaming system (e.g., a Microsoft Xbox gaming console with or without aKinect® gesture input device), and/or a personal messaging device,capable of communicating over network(s) 1210.

Although distributed system 1200 in FIG. 12 is shown with four clientcomputing devices, any number of client computing devices may besupported. Other devices, such as devices with sensors, etc., mayinteract with server 1212.

Network(s) 1210 in distributed system 1200 may be any type of networkfamiliar to those skilled in the art that can support datacommunications using any of a variety of available protocols, includingwithout limitation TCP/IP (transmission control protocol/Internetprotocol), SNA (systems network architecture), IPX (Internet packetexchange), AppleTalk, and the like. Merely by way of example, network(s)1210 can be a local area network (LAN), networks based on Ethernet,Token-Ring, a wide-area network, the Internet, a virtual network, avirtual private network (VPN), an intranet, an extranet, a publicswitched telephone network (PSTN), an infra-red network, a wirelessnetwork (e.g., a network operating under any of the Institute ofElectrical and Electronics (IEEE) 802.11 suite of protocols, Bluetooth®,and/or any other wireless protocol), and/or any combination of theseand/or other networks.

Server 1212 may be composed of one or more general purpose computers,specialized server computers (including, by way of example, PC (personalcomputer) servers, UNIX® servers, mid-range servers, mainframecomputers, rack-mounted servers, etc.), server farms, server clusters,or any other appropriate arrangement and/or combination. Server 1212 caninclude one or more virtual machines running virtual operating systems,or other computing architectures involving virtualization. One or moreflexible pools of logical storage devices can be virtualized to maintainvirtual storage devices for the server. Virtual networks can becontrolled by server 1212 using software defined networking. In variousembodiments, server 1212 may be adapted to run one or more services orsoftware applications described in the foregoing disclosure. Forexample, server 1212 may correspond to a server for performingprocessing as described above according to an embodiment of the presentdisclosure.

Server 1212 may run an operating system including any of those discussedabove, as well as any commercially available server operating system.Server 1212 may also run any of a variety of additional serverapplications and/or mid-tier applications, including HTTP (hypertexttransport protocol) servers, FTP (file transfer protocol) servers, CGI(common gateway interface) servers, JAVA® servers, database servers, andthe like. Exemplary database servers include without limitation thosecommercially available from Oracle, Microsoft, Sybase, IBM(International Business Machines), and the like.

In some implementations, server 1212 may include one or moreapplications to analyze and consolidate data feeds and/or event updatesreceived from users of client computing devices 1202, 1204, 1206, and1208. As an example, data feeds and/or event updates may include, butare not limited to, Twitter® feeds, Facebook® updates or real-timeupdates received from one or more third party information sources andcontinuous data streams, which may include real-time events related tosensor data applications, financial tickers, network performancemeasuring tools (e.g., network monitoring and traffic managementapplications), clickstream analysis tools, automobile trafficmonitoring, and the like. Server 1212 may also include one or moreapplications to display the data feeds and/or real-time events via oneor more display devices of client computing devices 1202, 1204, 1206,and 1208.

Distributed system 1200 may also include one or more databases 1214 and1216. These databases may provide a mechanism for storing informationsuch as user interactions information, usage patterns information,adaptation rules information, and other information used by embodimentsof the present invention. Databases 1214 and 1216 may reside in avariety of locations. By way of example, one or more of databases 1214and 1216 may reside on a non-transitory storage medium local to (and/orresident in) server 1212. Alternatively, databases 1214 and 1216 may beremote from server 1212 and in communication with server 1212 via anetwork-based or dedicated connection. In one set of embodiments,databases 1214 and 1216 may reside in a storage-area network (SAN).Similarly, any necessary files for performing the functions attributedto server 1212 may be stored locally on server 1212 and/or remotely, asappropriate. In one set of embodiments, databases 1214 and 1216 mayinclude relational databases, such as databases provided by Oracle, thatare adapted to store, update, and retrieve data in response toSQL-formatted commands.

In some embodiments, the document analysis and modification servicesdescribed above may be offered as services via a cloud environment. FIG.13 is a simplified block diagram of one or more components of a systemenvironment 1300 in which services may be offered as cloud services, inaccordance with an embodiment of the present disclosure. In theillustrated embodiment in FIG. 13 , system environment 1300 includes oneor more client computing devices 1304, 1306, and 1308 that may be usedby users to interact with a cloud infrastructure system 1302 thatprovides cloud services, including services for dynamically modifyingdocuments (e.g., webpages) responsive to usage patterns. Cloudinfrastructure system 1302 may comprise one or more computers and/orservers that may include those described above for server 1312.

It should be appreciated that cloud infrastructure system 1302 depictedin FIG. 13 may have other components than those depicted. Further, theembodiment shown in FIG. 13 is only one example of a cloudinfrastructure system that may incorporate an embodiment of theinvention. In some other embodiments, cloud infrastructure system 1302may have more or fewer components than shown in the figure, may combinetwo or more components, or may have a different configuration orarrangement of components.

Client computing devices 1304, 1306, and 1308 may be devices similar tothose described above for 1202, 1204, 1206, and 1208. Client computingdevices 1304, 1306, and 1308 may be configured to operate a clientapplication such as a web browser, a proprietary client application(e.g., Oracle Forms), or some other application, which may be used by auser of the client computing device to interact with cloudinfrastructure system 1302 to use services provided by cloudinfrastructure system 1302. Although exemplary system environment 1300is shown with three client computing devices, any number of clientcomputing devices may be supported. Other devices such as devices withsensors, etc. may interact with cloud infrastructure system 1302.

Network(s) 1310 may facilitate communications and exchange of databetween clients 1304, 1306, and 1308 and cloud infrastructure system1302. Each network may be any type of network familiar to those skilledin the art that can support data communications using any of a varietyof commercially-available protocols, including those described above fornetwork(s) 1310.

In certain embodiments, services provided by cloud infrastructure system1302 may include a host of services that are made available to users ofthe cloud infrastructure system on demand. In addition to servicesrelated to dynamic document modification responsive usage patterns,various other services may also be offered including without limitationonline data storage and backup solutions, Web-based e-mail services,hosted office suites and document collaboration services, databaseprocessing, managed technical support services, and the like. Servicesprovided by the cloud infrastructure system can dynamically scale tomeet the needs of its users.

In certain embodiments, a specific instantiation of a service providedby cloud infrastructure system 1302 may be referred to herein as a“service instance.” In general, any service made available to a user viaa communication network, such as the Internet, from a cloud serviceprovider's system is referred to as a “cloud service.” Typically, in apublic cloud environment, servers and systems that make up the cloudservice provider's system are different from the customer's ownon-premises servers and systems. For example, a cloud service provider'ssystem may host an application, and a user may, via a communicationnetwork such as the Internet, on demand, order and use the application.

In some examples, a service in a computer network cloud infrastructuremay include protected computer network access to storage, a hosteddatabase, a hosted web server, a software application, or other serviceprovided by a cloud vendor to a user, or as otherwise known in the art.For example, a service can include password-protected access to remotestorage on the cloud through the Internet. As another example, a servicecan include a web service-based hosted relational database and ascript-language middleware engine for private use by a networkeddeveloper. As another example, a service can include access to an emailsoftware application hosted on a cloud vendor's web site.

In certain embodiments, cloud infrastructure system 1302 may include asuite of applications, middleware, and database service offerings thatare delivered to a customer in a self-service, subscription-based,elastically scalable, reliable, highly available, and secure manner. Anexample of such a cloud infrastructure system is the Oracle Public Cloudprovided by the present assignee.

Cloud infrastructure system 1302 may also provide “big data” relatedcomputation and analysis services. The term “big data” is generally usedto refer to extremely large data sets that can be stored and manipulatedby analysts and researchers to visualize large amounts of data, detecttrends, and/or otherwise interact with the data. This big data andrelated applications can be hosted and/or manipulated by aninfrastructure system on many levels and at different scales. Tens,hundreds, or thousands of processors linked in parallel can act uponsuch data in order to present it or simulate external forces on the dataor what it represents. These data sets can involve structured data, suchas that organized in a database or otherwise according to a structuredmodel, and/or unstructured data (e.g., emails, images, data blobs(binary large objects), web pages, complex event processing). Byleveraging an ability of an embodiment to relatively quickly focus more(or fewer) computing resources upon an objective, the cloudinfrastructure system may be better available to carry out tasks onlarge data sets based on demand from a business, government agency,research organization, private individual, group of like-mindedindividuals or organizations, or other entity.

In various embodiments, cloud infrastructure system 1302 may be adaptedto automatically provision, manage and track a customer's subscriptionto services offered by cloud infrastructure system 1302. Cloudinfrastructure system 1302 may provide the cloud services via differentdeployment models. For example, services may be provided under a publiccloud model in which cloud infrastructure system 1302 is owned by anorganization selling cloud services (e.g., owned by Oracle Corporation)and the services are made available to the general public or differentindustry enterprises. As another example, services may be provided undera private cloud model in which cloud infrastructure system 1302 isoperated solely for a single organization and may provide services forone or more entities within the organization. The cloud services mayalso be provided under a community cloud model in which cloudinfrastructure system 1302 and the services provided by cloudinfrastructure system 1302 are shared by several organizations in arelated community. The cloud services may also be provided under ahybrid cloud model, which is a combination of two or more differentmodels.

In some embodiments, the services provided by cloud infrastructuresystem 1302 may include one or more services provided under Software asa Service (SaaS) category, Platform as a Service (PaaS) category,Infrastructure as a Service (IaaS) category, or other categories ofservices including hybrid services. A customer, via a subscriptionorder, may order one or more services provided by cloud infrastructuresystem 1302. Cloud infrastructure system 1302 then performs processingto provide the services in the customer's subscription order.

In some embodiments, the services provided by cloud infrastructuresystem 1302 may include, without limitation, application services,platform services and infrastructure services. In some examples,application services may be provided by the cloud infrastructure systemvia a SaaS platform. The SaaS platform may be configured to providecloud services that fall under the SaaS category. For example, the SaaSplatform may provide capabilities to build and deliver a suite ofon-demand applications on an integrated development and deploymentplatform. The SaaS platform may manage and control the underlyingsoftware and infrastructure for providing the SaaS services. Byutilizing the services provided by the SaaS platform, customers canutilize applications executing on the cloud infrastructure system.Customers can acquire the application services without the need forcustomers to purchase separate licenses and support. Various differentSaaS services may be provided. Examples include, without limitation,services that provide solutions for sales performance management,enterprise integration, and business flexibility for largeorganizations.

In some embodiments, platform services may be provided by cloudinfrastructure system 1302 via a PaaS platform. The PaaS platform may beconfigured to provide cloud services that fall under the PaaS category.Examples of platform services may include without limitation servicesthat enable organizations (such as Oracle) to consolidate existingapplications on a shared, common architecture, as well as the ability tobuild new applications that leverage the shared services provided by theplatform. The PaaS platform may manage and control the underlyingsoftware and infrastructure for providing the PaaS services. Customerscan acquire the PaaS services provided by cloud infrastructure system1302 without the need for customers to purchase separate licenses andsupport. Examples of platform services include, without limitation,Oracle Java Cloud Service (JCS), Oracle Database Cloud Service (DBCS),and others.

By utilizing the services provided by the PaaS platform, customers canemploy programming languages and tools supported by the cloudinfrastructure system and also control the deployed services. In someembodiments, platform services provided by the cloud infrastructuresystem may include database cloud services, middleware cloud services(e.g., Oracle Fusion Middleware services), and Java cloud services. Inone embodiment, database cloud services may support shared servicedeployment models that enable organizations to pool database resourcesand offer customers a Database as a Service in the form of a databasecloud. Middleware cloud services may provide a platform for customers todevelop and deploy various business applications, and Java cloudservices may provide a platform for customers to deploy Javaapplications, in the cloud infrastructure system.

Various different infrastructure services may be provided by an IaaSplatform in the cloud infrastructure system. The infrastructure servicesfacilitate the management and control of the underlying computingresources, such as storage, networks, and other fundamental computingresources for customers utilizing services provided by the SaaS platformand the PaaS platform.

In certain embodiments, cloud infrastructure system 1302 may alsoinclude infrastructure resources 1330 for providing the resources usedto provide various services to customers of the cloud infrastructuresystem. In one embodiment, infrastructure resources 1330 may includepre-integrated and optimized combinations of hardware, such as servers,storage, and networking resources to execute the services provided bythe PaaS platform and the SaaS platform, and other resources.

In some embodiments, resources in cloud infrastructure system 1302 maybe shared by multiple users and dynamically re-allocated per demand.Additionally, resources may be allocated to users in different timezones. For example, cloud infrastructure system 1302 may enable a firstset of users in a first time zone to utilize resources of the cloudinfrastructure system for a specified number of hours and then enablethe re-allocation of the same resources to another set of users locatedin a different time zone, thereby maximizing the utilization ofresources.

In certain embodiments, a number of internal shared services 1332 may beprovided that are shared by different components or modules of cloudinfrastructure system 1302 to enable provision of services by cloudinfrastructure system 1302. These internal shared services may include,without limitation, a security and identity service, an integrationservice, an enterprise repository service, an enterprise managerservice, a virus scanning and white list service, a high availability,backup and recovery service, service for enabling cloud support, anemail service, a notification service, a file transfer service, and thelike.

In certain embodiments, cloud infrastructure system 1302 may providecomprehensive management of cloud services (e.g., SaaS, PaaS, and IaaSservices) in the cloud infrastructure system. In one embodiment, cloudmanagement functionality may include capabilities for provisioning,managing and tracking a customer's subscription received by cloudinfrastructure system 1302, and the like.

In one embodiment, as depicted in FIG. 13 , cloud managementfunctionality may be provided by one or more modules, such as an ordermanagement module 1320, an order orchestration module 1322, an orderprovisioning module 1324, an order management and monitoring module1326, and an identity management module 1328. These modules may includeor be provided using one or more computers and/or servers, which may begeneral purpose computers, specialized server computers, server farms,server clusters, or any other appropriate arrangement and/orcombination.

In an exemplary operation, at 1334, a customer using a client device,such as client device 1304, 1306 or 1308, may interact with cloudinfrastructure system 1302 by requesting one or more services providedby cloud infrastructure system 1302 and placing an order for asubscription for one or more services offered by cloud infrastructuresystem 1302. In certain embodiments, the customer may access a cloudUser Interface (UI) such as cloud UI 1312, cloud UI 1314 and/or cloud UI1316 and place a subscription order via these UIs. The order informationreceived by cloud infrastructure system 1302 in response to the customerplacing an order may include information identifying the customer andone or more services offered by the cloud infrastructure system 1302that the customer intends to subscribe to.

At 1336, the order information received from the customer may be storedin an order database 1318. If this is a new order, a new record may becreated for the order. In one embodiment, order database 1318 can be oneof several databases operated by cloud infrastructure system 1318 andoperated in conjunction with other system elements.

At 1338, the order information may be forwarded to an order managementmodule 1320 that may be configured to perform billing and accountingfunctions related to the order, such as verifying the order, and uponverification, booking the order.

At 1340, information regarding the order may be communicated to an orderorchestration module 1322 that is configured to orchestrate theprovisioning of services and resources for the order placed by thecustomer. In some instances, order orchestration module 1322 may use theservices of order provisioning module 1324 for the provisioning. Incertain embodiments, order orchestration module 1322 enables themanagement of business processes associated with each order and appliesbusiness logic to determine whether an order should proceed toprovisioning.

As shown in the embodiment depicted in FIG. 13 , at 1342, upon receivingan order for a new subscription, order orchestration module 1322 sends arequest to order provisioning module 1324 to allocate resources andconfigure resources needed to fulfill the subscription order. Orderprovisioning module 1324 enables the allocation of resources for theservices ordered by the customer. Order provisioning module 1324provides a level of abstraction between the cloud services provided bycloud infrastructure system 1300 and the physical implementation layerthat is used to provision the resources for providing the requestedservices. This enables order orchestration module 1322 to be isolatedfrom implementation details, such as whether or not services andresources are actually provisioned on the fly or pre-provisioned andonly allocated/assigned upon request.

At 1344, once the services and resources are provisioned, a notificationmay be sent to the subscribing customers indicating that the requestedservice is now ready for use. In some instance, information (e.g., alink) may be sent to the customer that enables the customer to startusing the requested services.

At 1346, a customer's subscription order may be managed and tracked byan order management and monitoring module 1326. In some instances, ordermanagement and monitoring module 1326 may be configured to collect usagestatistics regarding a customer use of subscribed services. For example,statistics may be collected for the amount of storage used, the amountdata transferred, the number of users, and the amount of system up timeand system down time, and the like.

In certain embodiments, cloud infrastructure system 1300 may include anidentity management module 1328 that is configured to provide identityservices, such as access management and authorization services in cloudinfrastructure system 1300. In some embodiments, identity managementmodule 1328 may control information about customers who wish to utilizethe services provided by cloud infrastructure system 1302. Suchinformation can include information that authenticates the identities ofsuch customers and information that describes which actions thosecustomers are authorized to perform relative to various system resources(e.g., files, directories, applications, communication ports, memorysegments, etc.) Identity management module 1328 may also include themanagement of descriptive information about each customer and about howand by whom that descriptive information can be accessed and modified.

FIG. 14 illustrates an exemplary computer system 1300 that may be usedto implement an embodiment of the present invention. In someembodiments, computer system 1400 may be used to implement any of thevarious servers and computer systems described above. As shown in FIG.14 , computer system 1400 includes various subsystems including aprocessing unit 1404 that communicates with a number of peripheralsubsystems via a bus subsystem 1402. These peripheral subsystems mayinclude a processing acceleration unit 1406, an I/O subsystem 1408, astorage subsystem 1418 and a communications subsystem 1424. Storagesubsystem 1418 may include tangible computer-readable storage media 1422and a system memory 1410.

Bus subsystem 1402 provides a mechanism for letting the variouscomponents and subsystems of computer system 1400 communicate with eachother as intended. Although bus subsystem 1402 is shown schematically asa single bus, alternative embodiments of the bus subsystem may utilizemultiple buses. Bus subsystem 1402 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Forexample, such architectures may include an Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnect (PCI) bus, which can beimplemented as a Mezzanine bus manufactured to the IEEE P1386.1standard, and the like.

Processing subsystem 1404 controls the operation of computer system 1400and may comprise one or more processing units 1432, 1434, etc. Aprocessing unit may include be one or more processors, including singlecore or multicore processors, one or more cores of processors, orcombinations thereof. In some embodiments, processing subsystem 1404 caninclude one or more special purpose co-processors such as graphicsprocessors, digital signal processors (DSPs), or the like. In someembodiments, some or all of the processing units of processing subsystem1404 can be implemented using customized circuits, such as applicationspecific integrated circuits (ASICs), or field programmable gate arrays(FPGAs).

In some embodiments, the processing units in processing subsystem 1404can execute instructions stored in system memory 1410 or on computerreadable storage media 1422. In various embodiments, the processingunits can execute a variety of programs or code instructions and canmaintain multiple concurrently executing programs or processes. At anygiven time, some or all of the program code to be executed can beresident in system memory 1410 and/or on computer-readable storage media1422 including potentially on one or more storage devices. Throughsuitable programming, processing subsystem 1404 can provide variousfunctionalities described above for dynamically modifying documents(e.g., webpages) responsive to usage patterns.

In certain embodiments, a processing acceleration unit 1406 may beprovided for performing customized processing or for off-loading some ofthe processing performed by processing subsystem 1404 so as toaccelerate the overall processing performed by computer system 1400.

I/O subsystem 1408 may include devices and mechanisms for inputtinginformation to computer system 1400 and/or for outputting informationfrom or via computer system 1400. In general, use of the term “inputdevice” is intended to include all possible types of devices andmechanisms for inputting information to computer system 1400. Userinterface input devices may include, for example, a keyboard, pointingdevices such as a mouse or trackball, a touchpad or touch screenincorporated into a display, a scroll wheel, a click wheel, a dial, abutton, a switch, a keypad, audio input devices with voice commandrecognition systems, microphones, and other types of input devices. Userinterface input devices may also include motion sensing and/or gesturerecognition devices such as the Microsoft Kinect® motion sensor thatenables users to control and interact with an input device, theMicrosoft Xbox® 360 game controller, devices that provide an interfacefor receiving input using gestures and spoken commands. User interfaceinput devices may also include eye gesture recognition devices such asthe Google Glass® blink detector that detects eye activity (e.g.,“blinking” while taking pictures and/or making a menu selection) fromusers and transforms the eye gestures as input into an input device(e.g., Google Glass®). Additionally, user interface input devices mayinclude voice recognition sensing devices that enable users to interactwith voice recognition systems (e.g., Siri® navigator), through voicecommands.

Other examples of user interface input devices include, withoutlimitation, three dimensional (3D) mice, joysticks or pointing sticks,gamepads and graphic tablets, and audio/visual devices such as speakers,digital cameras, digital camcorders, portable media players, webcams,image scanners, fingerprint scanners, barcode reader 3D scanners, 3Dprinters, laser rangefinders, and eye gaze tracking devices.Additionally, user interface input devices may include, for example,medical imaging input devices such as computed tomography, magneticresonance imaging, position emission tomography, medical ultrasonographydevices. User interface input devices may also include, for example,audio input devices such as MIDI keyboards, digital musical instrumentsand the like.

User interface output devices may include a display subsystem, indicatorlights, or non-visual displays such as audio output devices, etc. Thedisplay subsystem may be a cathode ray tube (CRT), a flat-panel device,such as that using a liquid crystal display (LCD) or plasma display, aprojection device, a touch screen, and the like. In general, use of theterm “output device” is intended to include all possible types ofdevices and mechanisms for outputting information from computer system1400 to a user or other computer. For example, user interface outputdevices may include, without limitation, a variety of display devicesthat visually convey text, graphics and audio/video information such asmonitors, printers, speakers, headphones, automotive navigation systems,plotters, voice output devices, and modems.

Storage subsystem 1418 provides a repository or data store for storinginformation that is used by computer system 1400. Storage subsystem 1418provides a tangible non-transitory computer-readable storage medium forstoring the basic programming and data constructs that provide thefunctionality of some embodiments. Software (programs, code modules,instructions) that when executed by processing subsystem 1404 providethe functionality described above may be stored in storage subsystem1418. The software may be executed by one or more processing units ofprocessing subsystem 1404. Storage subsystem 1418 may also provide arepository for storing data used in accordance with the presentinvention.

Storage subsystem 1418 may include one or more non-transitory memorydevices, including volatile and non-volatile memory devices. As shown inFIG. 14 , storage subsystem 1418 includes a system memory 1410 and acomputer-readable storage media 1422. System memory 1410 may include anumber of memories including a volatile main random access memory (RAM)for storage of instructions and data during program execution and anon-volatile read only memory (ROM) or flash memory in which fixedinstructions are stored. In some implementations, a basic input/outputsystem (BIOS), containing the basic routines that help to transferinformation between elements within computer system 1400, such as duringstart-up, may typically be stored in the ROM. The RAM typically containsdata and/or program modules that are presently being operated andexecuted by processing subsystem 1404. In some implementations, systemmemory 1410 may include multiple different types of memory, such asstatic random access memory (SRAM) or dynamic random access memory(DRAM).

By way of example, and not limitation, as depicted in FIG. 14 , systemmemory 1410 may store application programs 1412, which may includeclient applications, Web browsers, mid-tier applications, relationaldatabase management systems (RDBMS), etc., program data 1414, and anoperating system 1416. By way of example, operating system 1416 mayinclude various versions of Microsoft Windows®, Apple Macintosh®, and/orLinux operating systems, a variety of commercially-available UNIX® orUNIX-like operating systems (including without limitation the variety ofGNU/Linux operating systems, the Google Chrome® OS, and the like) and/ormobile operating systems such as iOS, Windows® Phone, Android® OS,BlackBerry® 10 OS, and Palm® OS operating systems.

Computer-readable storage media 1422 may store programming and dataconstructs that provide the functionality of some embodiments. Software(programs, code modules, instructions) that when executed by processingsubsystem 1404 a processor provide the functionality described above maybe stored in storage subsystem 1418. By way of example,computer-readable storage media 1422 may include non-volatile memorysuch as a hard disk drive, a magnetic disk drive, an optical disk drivesuch as a CD ROM, DVD, a Blu-Ray® disk, or other optical media.Computer-readable storage media 1422 may include, but is not limited to,Zip® drives, flash memory cards, universal serial bus (USB) flashdrives, secure digital (SD) cards, DVD disks, digital video tape, andthe like. Computer-readable storage media 1422 may also include,solid-state drives (SSD) based on non-volatile memory such asflash-memory based SSDs, enterprise flash drives, solid state ROM, andthe like, SSDs based on volatile memory such as solid state RAM, dynamicRAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, andhybrid SSDs that use a combination of DRAM and flash memory based SSDs.Computer-readable media 1422 may provide storage of computer-readableinstructions, data structures, program modules, and other data forcomputer system 1400.

In certain embodiments, storage subsystem 1400 may also include acomputer-readable storage media reader 1420 that can further beconnected to computer-readable storage media 1422. Together and,optionally, in combination with system memory 1410, computer-readablestorage media 1422 may comprehensively represent remote, local, fixed,and/or removable storage devices plus storage media for storingcomputer-readable information.

In certain embodiments, computer system 1400 may provide support forexecuting one or more virtual machines. Computer system 1400 may executea program such as a hypervisor for facilitating the configuring andmanaging of the virtual machines. Each virtual machine may be allocatedmemory, compute (e.g., processors, cores), I/O, and networkingresources. Each virtual machine typically runs its own operating system,which may be the same as or different from the operating systemsexecuted by other virtual machines executed by computer system 1400.Accordingly, multiple operating systems may potentially be runconcurrently by computer system 1400. Each virtual machine generallyruns independently of the other virtual machines.

Communications subsystem 1424 provides an interface to other computersystems and networks. Communications subsystem 1424 serves as aninterface for receiving data from and transmitting data to other systemsfrom computer system 1400. For example, communications subsystem 1424may enable computer system 1400 to establish a communication channel toone or more client devices via the Internet for receiving and sendinginformation from and to the client devices.

Communication subsystem 1424 may support both wired and/or wirelesscommunication protocols. For example, in certain embodiments,communications subsystem 1424 may include radio frequency (RF)transceiver components for accessing wireless voice and/or data networks(e.g., using cellular telephone technology, advanced data networktechnology, such as 3G, 4G or EDGE (enhanced data rates for globalevolution), WiFi (IEEE 802.11 family standards, or other mobilecommunication technologies, or any combination thereof), globalpositioning system (GPS) receiver components, and/or other components.In some embodiments communications subsystem 1424 can provide wirednetwork connectivity (e.g., Ethernet) in addition to or instead of awireless interface.

Communication subsystem 1424 can receive and transmit data in variousforms. For example, in some embodiments, communications subsystem 1424may receive input communication in the form of structured and/orunstructured data feeds 1426, event streams 1428, event updates 1430,and the like. For example, communications subsystem 1424 may beconfigured to receive (or send) data feeds 1426 in real-time from usersof social media networks and/or other communication services such asTwitter® feeds, Facebook® updates, web feeds such as Rich Site Summary(RSS) feeds, and/or real-time updates from one or more third partyinformation sources.

In certain embodiments, communications subsystem 1424 may be configuredto receive data in the form of continuous data streams, which mayinclude event streams 1428 of real-time events and/or event updates1430, that may be continuous or unbounded in nature with no explicitend. Examples of applications that generate continuous data may include,for example, sensor data applications, financial tickers, networkperformance measuring tools (e.g., network monitoring and trafficmanagement applications), clickstream analysis tools, automobile trafficmonitoring, and the like.

Communications subsystem 1424 may also be configured to output thestructured and/or unstructured data feeds 1426, event streams 1428,event updates 1430, and the like to one or more databases that may be incommunication with one or more streaming data source computers coupledto computer system 1400.

Computer system 1400 can be one of various types, including a handheldportable device (e.g., an iPhone® cellular phone, an iPad® computingtablet, a PDA), a wearable device (e.g., a Google Glass® head mounteddisplay), a personal computer, a workstation, a mainframe, a kiosk, aserver rack, or any other data processing system.

Due to the ever-changing nature of computers and networks, thedescription of computer system 1400 depicted in FIG. 14 is intended onlyas a specific example. Many other configurations having more or fewercomponents than the system depicted in FIG. 14 are possible. Based onthe disclosure and teachings provided herein, a person of ordinary skillin the art will appreciate other ways and/or methods to implement thevarious embodiments.

Although specific embodiments of the invention have been described,various modifications, alterations, alternative constructions, andequivalents are also encompassed within the scope of the invention.Embodiments of the present invention are not restricted to operationwithin certain specific data processing environments, but are free tooperate within a plurality of data processing environments.Additionally, although embodiments of the present invention have beendescribed using a particular series of transactions and steps, it shouldbe apparent to those skilled in the art that the scope of the presentinvention is not limited to the described series of transactions andsteps. Various features and aspects of the above-described embodimentsmay be used individually or jointly.

Further, while embodiments of the present invention have been describedusing a particular combination of hardware and software, it should berecognized that other combinations of hardware and software are alsowithin the scope of the present invention.

Embodiments of the present invention may be implemented only inhardware, or only in software, or using combinations thereof. Thevarious processes described herein can be implemented on the sameprocessor or different processors in any combination. Accordingly, wherecomponents or modules are described as being configured to performcertain operations, such configuration can be accomplished, e.g., bydesigning electronic circuits to perform the operation, by programmingprogrammable electronic circuits (such as microprocessors) to performthe operation, or any combination thereof. Processes can communicateusing a variety of techniques including but not limited to conventionaltechniques for interprocess communication, and different pairs ofprocesses may use different techniques, or the same pair of processesmay use different techniques at different times.

The specification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense. It will, however, beevident that additions, subtractions, deletions, and other modificationsand changes may be made thereunto without departing from the broaderspirit and scope as set forth in the claims. Thus, although specificinvention embodiments have been described, these are not intended to belimiting. Various modifications and equivalents are within the scope ofthe following claims.

The data enrichment service described herein may also be referred to asIMI, ODECS, and/or Big Data Prep.

What is claimed is:
 1. A method comprising: receiving, by a computingsystem of a data enrichment system, a request to access a data sourcefrom the data enrichment system, wherein the request includesidentification information of the data source and a source type of thedata source; determining, by the computing system, using theidentification information, that the data enrichment system does nothave connection parameters for establishing a connection to the datasource; in response to determining that the data enrichment system doesnot have the connection parameters for establishing the connection tothe data source, rendering a user interface to receive connectioninformation for the data source, the connection information based on thesource type; receiving, by the computing system, via the user interface,a set of connection parameters, the set of connection parametersincluding a connection name, a connection type, a data source location,and credential information; storing the set of connection parameters ina data repository system accessible to the data enrichment system;establishing, by the computing system, using the set of connectionparameters received via the user interface, the connection between thedata enrichment system and the data source; and generating, by thecomputing system, a profile for a data set accessed from the data sourcevia the connection.
 2. The method of claim 1, further comprising:generating, by the data enrichment system, a data service correspondingto the request; storing, using a data structure in the data repositorysystem, the set of connection parameters in association with the dataservice; and generating, by the data service, a transformed data setbased on the data set accessed from the data source, wherein thetransformed data set is generated using the profile.
 3. The method ofclaim 1, wherein the source type includes a cloud-based storage system,a distributed storage system, a web service, or a uniform resourcelocator (URL).
 4. The method of claim 1, further comprising: querying,using the identification information, the data repository system todetermine whether the data enrichment system has the connection to thedata source; upon determining that the data enrichment system has theconnection to the data source, accessing from the data repositorysystem, the connection information for the connection; and rendering theuser interface to indicate the connection information for the connectionto the data source.
 5. The method of claim 1, wherein the credentialinformation includes an access key and a password defined for access tothe data source.
 6. The method of claim 1, further comprising:accessing, using the identification information, the data repositorysystem to determine that the data enrichment system has a plurality ofconnections to the data source; rendering a user interface thatidentifies each of the plurality of connections to the data source; andreceiving, via the user interface that identifies each of the pluralityof connections, input indicating a selection of one of the plurality ofconnections.
 7. The method of claim 1, wherein storing the set ofconnection parameters and the credential information includes:generating one or more data structures defining the data source, the oneor more data structures including the set of connection parameters andthe credential information, wherein the one or more data structures arestored in the data repository system.
 8. A data enrichment systemcomprising: a plurality of data sources; a data repository system; and acloud computing infrastructure system comprising: one or more processorscommunicatively coupled to the plurality of data sources over at leastone communication network; and a memory coupled to the one or moreprocessors, the memory storing instructions to provide a data enrichmentservice, wherein the instructions, when executed by the one or moreprocessors, cause the one or more processors to: receive a request toaccess a data source of the plurality of data sources, wherein therequest includes identification information of the data source and asource type of the data source; determine, using the identificationinformation, that the data enrichment system does not have connectionparameters for establishing a connection to the data source; in responseto determining that the data enrichment system does not have theconnection parameters for establishing the connection to the datasource, render a user interface to receive connection information forthe data source, the connection information based on the source type;receive, via the user interface, a set of connection parameters, the setof connection parameters including a connection name, a connection type,a data source location, and credential information; store the set ofconnection parameters in the data repository system accessible to thedata enrichment system; establish, using the set of connectionparameters, the connection between the data enrichment system and thedata source; and generate a profile for a data set accessed from thedata source via the connection.
 9. The data enrichment system of claim8, wherein the instructions, when executed by the one or moreprocessors, further cause the one or more processors to: generate a dataservice corresponding to the request; store, using a data structure inthe data repository system, the set of connection parameters inassociation with the data service; and generate, by the data service, atransformed data set based on the data set accessed from the datasource, wherein the transformed data set is generated using the profile.10. The data enrichment system of claim 8, wherein the source typeincludes a cloud-based storage system, a distributed storage system, aweb service, or a uniform resource locator (URL).
 11. The dataenrichment system of claim 8, wherein the instructions, when executed bythe one or more processors, further cause the one or more processors to:query, using the identification information, the data repository systemto determine whether the data enrichment system has a connection to thedata source; upon determining that the data enrichment system has aconnection to the data source, access from the data repository system,the connection information for the connection; and render the userinterface to indicate the connection information for the connection tothe data source.
 12. The data enrichment system of claim 8, wherein thecredential information includes an access key and a password defined foraccess to the data source.
 13. The data enrichment system of claim 8,wherein the instructions, when executed by the one or more processors,further cause the one or more processors to: access, using theidentification information, the data repository system to determine thatthe data enrichment system has a plurality of connections to the datasource; render a user interface that identifies each of the plurality ofconnections to the data source; and receive, via the user interface thatidentifies each of the plurality of connections, input indicating aselection of one of the plurality of connections.
 14. The dataenrichment system of claim 8, wherein storing the set of connectionparameters and the credential information includes: generating one ormore data structures defining the data source, the one or more datastructures including the set of connection parameters and the credentialinformation, wherein the one or more data structures are stored in thedata repository system.
 15. A non-transitory computer readable storagemedium including instructions stored thereon which, when executed by oneor more processors, cause the one or more processors to: receive, by acomputing system of a data enrichment system, a request to access a datasource from the data enrichment system, wherein the request includesidentification information of the data source and a source type of thedata source; determine, by the computing system, using theidentification information, that the data enrichment system does nothave connection parameters for establishing a connection to the datasource; in response to determining that the data enrichment system doesnot have the connection parameters for establishing the connection tothe data source, render a user interface to receive connectioninformation for the data source, the connection information based on thesource type; receive, by the computing system, via the user interface, aset of connection parameters, the set of connection parameters includinga connection name, a connection type, a data source location, andcredential information; store the set of connection parameters in a datarepository system accessible to the data enrichment system; establish,by the computing system, using the set of connection parameters receivedvia the user interface, a connection between the data enrichment systemand the data source; and generate, by the computing system, a profilefor a data set accessed from the data source via the connection.
 16. Thenon-transitory computer readable storage medium of claim 15, wherein theinstructions, when executed by the one or more processors, further causethe one or more processors to: generate, by the data enrichment system,a data service corresponding to the request; store, using a datastructure in the data repository system, the set of connectionparameters in association with the data service; and generate, by thedata service, a transformed data set based on the data set accessed fromthe data source, wherein the transformed data set is generated using theprofile.
 17. The non-transitory computer readable storage medium ofclaim 15, wherein the instructions, when executed by the one or moreprocessors, further cause the one or more processors to: query, usingthe identification information, the data repository system to determinewhether the data enrichment system has a connection to the data source;upon determining that the data enrichment system has a connection to thedata source, access from the data repository system, the connectioninformation for the connection; and render the user interface toindicate the connection information for the connection to the datasource.
 18. The non-transitory computer readable storage medium of claim15, wherein the credential information includes an access key and apassword defined for access to the data source.
 19. The non-transitorycomputer readable storage medium of claim 15, wherein the instructions,when executed by the one or more processors, further cause the one ormore processors to: access, using the identification information, thedata repository system to determine that the data enrichment system hasa plurality of connections to the data source; render a user interfacethat identifies each of the plurality of connections to the data source;and receive, via the user interface that identifies each of theplurality of connections, input indicating a selection of one of theplurality of connections.
 20. The non-transitory computer readablestorage medium of claim 15, wherein storing the set of connectionparameters and the credential information includes: generating one ormore data structures defining the data source, the one or more datastructures including the set of connection parameters and the credentialinformation, wherein the one or more data structures are stored in thedata repository system.